Topic: pattern recognition - Searchworks@Jio Institute Digital Library Search Results

90 results

1. A Highly Accurate pH Detection Method for Sweat Analysis using a Printed 96‐Microwell Colorimetric Sensor Array.

Author: Sasaki, Yui, Lyu, Xiaojun, and Minami, Tsuyoshi
Abstract: This work reports on a facile 96‐microwell paper‐based colorimetric sensor array device (PCSAD) for highly accurate pH detection toward sweat analysis. Although a pH test strip has been widely used to easily visualize pH conditions with naked eyes, its accuracy to guarantee below the decimal point in pH changes has still been a concern. In contrast, the PCSAD could detect slight pH changes because of abundant chemical information by the array design. The color gradients on the PCSAD upon changing pH conditions can be rapidly recorded by a flatbed scanner. The captured digital images of the PCSAD were treated with imaging analysis, followed by extraction of color intensities for inset data. By employing the partially overlapped detectable pH ranges of pH indicators, seamless inset data was obtained. Therefore, the PCSAD successfully discriminated against slight changes in pH conditions in human sweat with high accuracy by pattern recognition methods. [ABSTRACT FROM AUTHOR]
Published: 2023
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2. Printed 384‐Well Microtiter Plate on Paper for Fluorescent Chemosensor Arrays in Food Analysis.

Author: Lyu, Xiaojun, Sasaki, Yui, Ohshiro, Kohei, Tang, Wei, Yuan, Yousi, and Minami, Tsuyoshi
Subjects: *FOOD chemistry, *MICROPLATES, *AMINO acid analysis, *IMAGING systems, *IMAGE analysis
Abstract: We propose a printed 384‐well microtiter paper‐based fluorescent chemosensor array device (384‐well microtiter PCAD) to simultaneously categorize and discriminate saccharides and sulfur‐containing amino acids for food analysis. The 384‐well microtiter PCAD requiring 1 μL/4 mm2 of each well can allow high‐throughput sensing. The device embedded with self‐assembled fluorescence chemosensors displayed a fingerprint‐like response pattern for targets, the image of which was rapidly captured by a portable digital camera. Indeed, the paper‐based chemosensor array system combined with imaging analysis and pattern recognition techniques not only successfully categorized saccharides and sulfur‐containing amino acids but also classified mono‐ and disaccharide groups. Furthermore, the quantitative detectability of the printed device was revealed by a spike and recovery test for fructose and glutathione in a diluted freshly made tomato juice. We believe that the 384‐well microtiter PCAD using the imaging analysis system will be a powerful sensor for multi‐analytes at several categorized groups in real samples. [ABSTRACT FROM AUTHOR]
Published: 2022
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3. Paper-based optical sensor arrays for simultaneous detection of multi-targets in aqueous media: A review.

Author: Mohan, Binduja, Sasaki, Yui, and Minami, Tsuyoshi
Subjects: *SENSOR arrays, *OPTICAL sensors, *ELECTRONIC noses, *ENVIRONMENTAL monitoring, *IMAGE analysis, *FOOD safety, *SPECTROPHOTOMETERS
Abstract: Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media. [Display omitted] • Cross-reactive sensors are applied to paper-based arrays for simultaneous detection. • Portable digital recorders can be used for detecting various responses. • Imaging analysis techniques can accelerate accurate data processing. [ABSTRACT FROM AUTHOR]
Published: 2024
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4. Front Cover: A Highly Accurate pH Detection Method for Sweat Analysis using a Printed 96‐Microwell Colorimetric Sensor Array (Anal. Sens. 5/2023).

Author: Sasaki, Yui, Lyu, Xiaojun, and Minami, Tsuyoshi
Published: 2023
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5. Integrated metabolomics coupled with pattern recognition and pathway analysis to reveal molecular mechanism of cadmium-induced diabetic nephropathy

Author: Chang Xiangna, Fuxin Chen, Wenjuan Yang, Mengrao Wang, Pin Gong, and Lan Wang
Subjects: Paper, 0301 basic medicine, 030213 general clinical medicine, Ceramide, business.industry, Health, Toxicology and Mutagenesis, Wnt signaling pathway, Pattern recognition, Toxicology, medicine.disease, Blot, Diabetic nephropathy, 03 medical and health sciences, chemistry.chemical_compound, 030104 developmental biology, 0302 clinical medicine, Metabolomics, Lysophosphatidylcholine, chemistry, medicine, Artificial intelligence, business, Sphingomyelin, Toxicant
Abstract: Diabetic nephropathy (DN) is becoming a worldwide public health problem and its pathophysiological mechanism is not well understood. Emerging evidences indicated that cadmium (Cd), an industrial material but also an environmental toxin, may be involved in the development and progression of diabetes and diabetes-related kidney disease. However, the underlying mechanism is still unclear. Herein, a DN animal model was constructed by exposing to Cd, the metabolomic profiling of DN mice were obtained by using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS), pattern recognition and pathway analysis were performed to screen potential biomarker. Moreover, western blotting was employed to verify the possible mechanism involved in the occurrence of Cd-induced DN. A total of 66 metabolites in serum have been screened out and identified as biomarkers, including free fatty acids, phospholipids, sphingomyelins, glycerides, and others. Significant differences were demonstrated between the metabolic profiles, including decreased levels of phospholipid and increased content of triglyceride, diacylglycerols, ceramide, lysophosphatidylcholine in Cd-induced DN mice compared with control. Protein expression level of p38 MAPK and Wnt/β-catenin were significantly increased. UPLC-Q-TOF/MS-based serum metabolomics coupled with pattern recognition methods and pathway analysis provide a powerful approach to identify potential biomarkers and is a new strategy to predict the underlying mechanism of disease caused by environmental toxicant.
Published: 2021

6. EVALUATION OF VGC ANALYZER BY COMPARISON WITH GOLD STANDARD ROC SOFTWARE AND ANALYSIS OF SIMULATED VISUAL GRADING DATA

Author: Lars Gunnar Månsson, Jonny Hansson, and Magnus Båth
Subjects: Paper, Spectrum analyzer, Computer science, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Software, Resampling, Radiology, Nuclear Medicine and imaging, Paired Data, AcademicSubjects/SCI00180, Radiation, Radiological and Ultrasound Technology, Receiver operating characteristic, business.industry, Public Health, Environmental and Occupational Health, Pattern recognition, General Medicine, Gold standard (test), Rejection rate, Confidence interval, ROC Curve, 030220 oncology & carcinogenesis, Artificial intelligence, business
Abstract: The purpose of the present work was to evaluate the use of resampling statistical methods for analysis of visual grading data—implemented in the software VGC Analyzer—by comparing the reanalyzed results from previously performed visual grading studies with the results calculated by gold standard receiver operating characteristic (ROC) methodology, Obuchowski-Rockette (OR)-Dorfman–Berbaum–Metz (DBM) multiple-readers and multiple-case (MRMC) and by analysis of simulated visual grading data where the true distribution was presumed to be known. The reanalysis was performed on two multiple-reader studies with non-paired data and paired data, respectively. The simulation study was performed by simulating a large number of visual grading characteristics (VGC) studies and by analyzing the statistical distribution of null hypothesis (H0) rejection rate. The comparison with OR-DBM MRMC showed good agreement when analyzing non-paired data for both fixed-reader and random-reader settings for the calculated area under the curve values and the confidence intervals (CIs). For paired data analysis, VGC Analyzer showed significantly lower CIs compared with the ROC software. This effect was also illustrated by the simulation study, where the VGC Analyzer, in general, showed good accuracy for simulated studies with stable statistical basis. For simulated studies with unstable statistics, the accuracy in the H0 rejection rate decreased. The present study has shown that resampling methodology can be used to accurately perform the statistical analysis of a VGC study, although the resampling technique used makes the method sensitive to small data sets.
Published: 2021

7. IMPROVEMENTS OF 111IN SPECT IMAGES RECONSTRUCTED WITH SPARSELY ACQUIRED PROJECTIONS BY DEEP LEARNING GENERATED SYNTHETIC PROJECTIONS

Author: Peter Bernhardt, Johanna Svensson, M Van Essen, W Emma, and Tobias Rydén
Subjects: Paper, Computer science, Image quality, 030218 nuclear medicine & medical imaging, law.invention, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Post filtering, law, Image Processing, Computer-Assisted, medicine, Radiology, Nuclear Medicine and imaging, Tomography, Emission-Computed, Single-Photon, Image quality degradation, AcademicSubjects/SCI00180, Radiation, Radiological and Ultrasound Technology, medicine.diagnostic_test, Phantoms, Imaging, business.industry, Deep learning, Attenuation, Indium Radioisotopes, Public Health, Environmental and Occupational Health, Collimator, Pattern recognition, General Medicine, 030220 oncology & carcinogenesis, Acquisition time, Artificial intelligence, business, Emission computed tomography
Abstract: The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30–120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30–120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.
Published: 2021

8. Likelihood Ratios for Deep Neural Networks in Face Comparison

Author: Andrea Macarulla Rodriguez, Zeno Geradts, Marcel Worring, Intelligent Sensory Information Systems (IVI, FNWI), and Operations Management (ABS, FEB)
Subjects: Paper, Computer science, Automated Facial Recognition, Kernel density estimation, ENFSI proficiency test, Sensitivity and Specificity, 01 natural sciences, Convolutional neural network, Pathology and Forensic Medicine, 03 medical and health sciences, 0302 clinical medicine, Software, Genetics, face verification likelihood ratio, Humans, 030216 legal & forensic medicine, Parametric statistics, Likelihood Functions, Commercial software, Models, Statistical, business.industry, Deep learning, Forensic Sciences, 010401 analytical chemistry, Nonparametric statistics, deep learning, Confusion matrix, Pattern recognition, 0104 chemical sciences, digital forensic science, Papers, Digital & Multimedia Sciences, Neural Networks, Computer, Artificial intelligence, business, face recognition
Abstract: In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state‐of‐the‐art free software was preferred over commercial software. Three different open‐source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel density estimation, and isotonic regression with pool adjacent violators algorithm. The results show that with low‐quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. The rank correlation between investigators and software is around 80%. We conclude that the software can assist in reporting officers as it can do faster and more reliable comparisons with full‐frontal images, which can help the forensic expert in casework.
Published: 2020

9. Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks

Author: J. Griffiths, Antonin Vacheret, Steven Kleinegesse, Robert A. Taylor, D. Saunders, Vacheret, A [0000-0001-7792-4349], and Apollo - University of Cambridge Repository
Subjects: Paper, Photomultiplier, Physics - Instrumentation and Detectors, Computer science, FOS: Physical sciences, Scintillator, 01 natural sciences, Convolutional neural network, 6Li F:ZnS(Ag), pulse shape discrimination, Silicon photomultiplier, Artificial Intelligence, 0103 physical sciences, convolutional neural networks, 010306 general physics, Continuous wavelet transform, Scintillation, Artificial neural network, 010308 nuclear & particles physics, business.industry, Detector, Li-6 F:ZnS(Ag), Pattern recognition, Instrumentation and Detectors (physics.ins-det), t-SNE, Human-Computer Interaction, machine learning, Artificial intelligence, business, Software
Abstract: We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised SiPM signals from a dual scintillator detector element made of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilsing a separate PMT was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and is found to provide superior levels of discrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods., 13 pages, 8 figures, submitted to Nucl. Instr. Meth. A
Published: 2022
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10. COVID-19 detection method based on SVRNet and SVDNet in lung x-rays

Author: Ziqi Hu, Kedong Rao, Jianbiao He, Chang Wen, Kai Xie, and Xiao-Long Guo
Subjects: Paper, 2019-20 coronavirus outbreak, Lung, Coronavirus disease 2019 (COVID-19), Contextual image classification, business.industry, Radiography, Digital Pathology, COVID-19, deep learning, Pattern recognition, transfer learning, x-ray images, medicine.anatomical_structure, Image database, Test set, Medicine, Radiology, Nuclear Medicine and imaging, Artificial intelligence, Sensitivity (control systems), business
Abstract: Purpose: To detect and diagnose coronavirus disease 2019 (COVID-19) better and faster, separable VGG-ResNet (SVRNet) and separable VGG-DenseNet (SVDNet) models are proposed, and a detection system is designed, based on lung x-rays to diagnose whether patients are infected with COVID-19. Approach: Combining deep learning and transfer learning, 1560 lung x-ray images in the COVID-19 x-ray image database (COVID-19 Radiography Database) were used as the experimental data set, and the most representative image classification models, VGG16, ResNet50, InceptionV3, and Xception, were fine-tuned and trained. Then, two new models for lung x-ray detection, SVRNet and SVDNet, were proposed on this basis. Finally, 312 test set images (including 44 COVID-19 and 268 normal images) were used as input to evaluate the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet models. Results: In the classification experiment of lung x-rays that tested positive and negative for COVID-19, the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet are 99.13%, 99.14%, 99.12% and 99.37%, 99.43%, 99.31%, respectively. Compared with the VGG16 network, SVRNet and SVDNet increased by 3.07%, 2.84%, 3.31% and 3.31%, 3.13%, 3.50%, respectively. On the other hand, the parameters of SVRNet and SVDNet are 5.65 × 10 6 and 6.57 × 10 6 , respectively. These are 61.56% and 55.31% less than VGG16, respectively. Conclusions: The SVRNet and SVDNet models proposed greatly reduce the number of parameters, while improving the accuracy and increasing the operating speed, and can accurately and quickly detect lung x-rays containing COVID-19.
Published: 2021

11. Detection of COVID-19 from chest x-ray images using transfer learning

Author: Fatemeh Zabihollahy, Jenita Manokaran, Andrew Hamilton-Wright, and Eranga Ukwatta
Subjects: Paper, Contextual image classification, business.industry, Deep learning, Digital Pathology, Feature extraction, COVID-19, deep learning, Pattern recognition, Gold standard (test), transfer learning, chest x-ray, pretrained models, Multiclass classification, Binary classification, Feature (computer vision), Medical imaging, Medicine, Radiology, Nuclear Medicine and imaging, Artificial intelligence, business
Abstract: Coronavirus 2019 (SARS-CoV-2 or COVID-19) is an infectious disease affecting the respiratory system, which has caused a global pandemic and widespread morbidity and mortality in humans. It has mild to severe symptoms similar to those of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), resulting in multiorgan damage.1 Currently, two tests are available for detection of COVID-19 in the affected patients: diagnostic tests (current infection) and antibody tests (past infection). Diagnostic tests, such as reverse transcription polymerase chain reaction (RT-PCR) and antigen tests, are used for rapid diagnosis of COVID-19. Since false positives (FPs) are more common in antigen tests, RT-PCR is used as the gold standard for diagnosis of the disease. RT-PCR tests require an intensive lab work to acquire the results,2 and the cost of the test is a major concern in many countries that have a private health system. Although the PCR and antigen test can now provide a rapid diagnosis, the assessment of the lungs using medical imaging will provide information on disease burden. Also, faster and earlier detection of COVID-19 would help in isolating the affected patients sooner to alleviate the disease spread. Chest radiography (CXR) and computed tomography (CT) images are the conventional medical imaging modalities used in lung disease diagnosis.3,4 Though CT images are extensively used in the COVID-19 diagnosis,5–7 cost8 and radiation exposure are major concerns. CXR are preferred over CT images as they have less exposure to radiation and extensively available.9 Hence, in this study, CXR images are used for automatic diagnosis of COVID-19. Deep learning techniques are widely used in various fields, such as computer vision, machine vision, and speech recognition, among which computer vision is one of the most popular fields in which promising results to have been obtained in image classification tasks.10–12 In medical image analysis, deep learning has been widely investigated for computer-aided diagnosis and treatment.13,14 Several state-of-art methods have been proposed for the diagnosis of COVID-19 using CXR images15–19 and CT images5,20,21 based on deep learning techniques. The transfer learning approaches based on deep learning have been preferred in detection of COVID-19 due to the limited available dataset. Several studies have been implemented for COVID-19 diagnosis using the pretrained model as a feature extractor by implementing transfer learning techniques.22–24 Table 1 summarizes recent studies describing methods developed for detecting COVID-19 from CXR images; the overall datasets used for training and testing the model, accuracy, and the sensitivity are provided. Ozturk et al.18 developed the DarkCovidNet model for the detection of COVID-19 using multiclass classification. The model was developed using end-to-end architecture without adding any feature extraction techniques. Sensitivity of 85.35% was achieved by the proposed model. Wang and Wong25 proposed COVID-Net, a deep convolutional neural network (DCNN) developed by adopting machine-driven design exploration strategy. A multiclass classification was performed using CXR images in identifying COVID-19 from normal and non-COVID disease (i.e., pneumonia). Both qualitative and quantitative analysis were performed to determine the success of the model. The developed model was able to achieve a sensitivity of 91% for detecting COVID-19. Panwar et al.26 introduced nCOVnet, an algorithm developed based on a transfer learning model for faster diagnosis of COVID-19 from CXR images. The proposed model is built using VGG 16 as the base model. The model was able to achieve 97.62% in detecting COVID-19. Sethy et al.23 described a comparative study on Xception, ResNet18, ResNet50, Resnet 101, Inceptionv3, Inceptionresnetv2, GoogleNet, Densenet201, VGG16, VGG19, and AlexNet using the deep features extracted from the CNN layers and fed to the SVM classifier for classification. ResNet50 performed better compared with other classification models on a binary classification between viral pneumonia and COVID-19 with a classification accuracy of 95.38%. Jain et al.27 utilized ResNet50 model that was used in stage-I network model for distinguishing viral (including COVID-19) from bacterial pneumonia and normal cases using CXR images. Based on the result obtained, ResNet101 was used in stage-II network model to classify COVID-19 from other viral pneumonia. The model achieved an accuracy of 97% in COVID-19 detection. Narin et al.28 proposed three CNNs for the detection of COVID-19 through CXR images. The pretrained models chosen for comparative study were ResNet50, InceptionV3, and Inception-ResNetV2. A binary classification was performed between normal and COVID-19. ResNet50 model outperformed all the other models with the highest classification accuracy of 98%. Ioannis et al.22 compared VGG 19, Mobile Net, Inception, Xception, and Inception ResNetv2 pretrained models for the automatic detection of COVID-19 using CXR images. They implemented multiclass classification among COVID-19, normal, and pneumonia. MobileNetV2 achieved highest sensitivity rate of 98.6%. Asnaoui et al.29 put forward a comparative study on the pretrained DCNN models, namely VGG16, VGG19, Inception-ResNetV2, InceptionV3, ResNet50, DenseNet201, and MobileNetV2 for the classification of CXR into normal, bacteria, and coronavirus (multiclass classification). Inception-ResNetV2 model provided a sensitivity rate of 92.11% in detecting coronavirus. Hemdan et al.30 proposed COVIDX-Net a deep learning framework based on seven DCNNs namely, VGG19, Xception, ResNetV2, InceptionV3, Inception-ResNetV2, DenseNet201, and MobileNetV2 for the diagnosis of COVID-19 using x-ray images. The VGG19 and DenseNet201 achieved an accuracy of 90% compared with other models with F1-score of 0.89 for normal and 0.91 for COVID-19. Table 1 Methods described for diagnosis of COVID-19 from CXR and CT images.
Published: 2021

12. Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications

Author: Iwan W. Schie, Clara Stiebing, and Jürgen Popp
Subjects: Paper, Fluorescence-lifetime imaging microscopy, genetic structures, Computer science, Biomedical Engineering, Spectrum Analysis, Raman, label-free, Biomaterials, Biological specimen, fluorescence imaging, Optical coherence tomography, medicine, Set (psychology), optical coherence tomography, Modality (human–computer interaction), Modalities, medicine.diagnostic_test, business.industry, Optical Imaging, Pattern recognition, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, in vivo, Autofluorescence, Microscopy, Fluorescence, Raman spectroscopy, Artificial intelligence, business, Tomography, Optical Coherence, Preclinical imaging, Perspectives
Abstract: Raman spectroscopy has shown very promising results in medical diagnostics by providing label-free and highly specific molecular information of pathological tissue ex vivo and in vivo. Nevertheless, the high specificity of Raman spectroscopy comes at a price, i.e., low acquisition rate, no direct access to depth information, and limited sampling areas. However, a similar case regarding advantages and disadvantages can also be made for other highly regarded optical modalities, such as optical coherence tomography, autofluorescence imaging and fluorescence spectroscopy, fluorescence lifetime microscopy, second-harmonic generation, and others. While in these modalities the acquisition speed is significantly higher, they have no or only limited molecular specificity and are only sensitive to a small group of molecules. It can be safely stated that a single modality provides only a limited view on a specific aspect of a biological specimen and cannot assess the entire complexity of a sample. To solve this issue, multimodal optical systems, which combine different optical modalities tailored to a particular need, become more and more common in translational research and will be indispensable diagnostic tools in clinical pathology in the near future. These systems can assess different and partially complementary aspects of a sample and provide a distinct set of independent biomarkers. Here, we want to give an overview on the development of multimodal systems that use RS in combination with other optical modalities to improve the diagnostic performance.
Published: 2021

13. Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

Author: Thanh-Hai Le, Ngan Thanh Luu, Quoc Hung Phan, and Thi-Thu-Hien Pham
Subjects: Paper, Skin Neoplasms, Optical Phenomena, Biomedical Engineering, Polarimetry, Stokes–Mueller matrix formalism, Biomaterials, Humans, Mueller calculus, General, Circular polarization, Skin, Mathematics, Linear polarization, business.industry, Carcinoma, Pattern recognition, Polarization (waves), Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Random forest, human skin cancer, Data point, classification, Artificial intelligence, business, Classifier (UML), Algorithms, random forest
Abstract: Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier. Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.
Published: 2021

14. Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data

Author: Dimo Ivanov, Michael Lührs, Bettina Sorger, Amaia Benitez-Andonegui, Laurien Nagels-Coune, Rainer Goebel, Vision, RS: FPN CN 1, MRI, and RS: FPN CN 5
Subjects: Paper, CORTEX, Computer science, Neuroscience (miscellaneous), COMMUNICATION, 01 natural sciences, Signal, 010309 optics, 03 medical and health sciences, brain-computer interfaces, 0302 clinical medicine, brain–computer interfaces, MENTAL ROTATION, 0103 physical sciences, medicine, functional near-infrared spectroscopy, Radiology, Nuclear Medicine and imaging, Sensitivity (control systems), optode layout design, BRAIN, PHOTON MIGRATION, Brain–computer interface, mental imagery, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Probabilistic logic, Pattern recognition, DIFFUSE OPTICAL TOMOGRAPHY, TASKS, REGIONS, Research Papers, functional magnetic resonance imaging, Functional near-infrared spectroscopy, Artificial intelligence, Optode, Neurofeedback, business, Functional magnetic resonance imaging, MOTOR, 030217 neurology & neurosurgery, SYSTEM
Abstract: Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain–computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.
Published: 2021

15. Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis

Author: Luxi Weng, Jun Lin, Hanshen Chen, Jiaqi Shao, and Huiping Lin
Subjects: Paper, Computer science, smartphone-based imaging, Biomedical Engineering, Convolutional neural network, Imaging, Biomaterials, Deep Learning, Region of interest, medicine, oral potentially malignant disorders, Humans, Segmentation, Survival rate, Early Detection of Cancer, Leukoplakia, Retrospective Studies, oral cancer diagnosis, business.industry, Deep learning, Pattern recognition, medicine.disease, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Feature (computer vision), image collection, Oral lichen planus, Mouth Neoplasms, Artificial intelligence, Smartphone, business
Abstract: Oral cancer is one of the most common cancers worldwide, with high mortality rates. According to the International Agency for Research on Cancer, there were an estimated 377,000 new cases of lip and oral cavity cancers in 2020, with nearly 177,000 deaths worldwide.1 Despite advances in oncology therapy, mortality rates for oral cancer remain high over the past few decades. A majority of oral cancer patients do not have access to timely, quality diagnosis, and treatment, especially in rural areas, resulting in poor survival rates. The overall 5-year survival rate of diagnosed oral cancer patients is around 50% and has varied by race and area,2 whereas survival rates as high as 65% have been reported in developed countries; in some rural areas, they can be as low as 15% depending on the affected part of the oral cavity. The 2020 Cancer Statistics Report from India estimated that 66.6% of patients suffering from head and neck cancer were diagnosed at the locally advanced stage.3 In short, patient survival rates and prognosis are severely compromised when oral cancer patients are diagnosed at more advanced stages4,5 so that enhancing early diagnosis could mean a significant rise in positive survival outcomes.6,7 As oral squamous cell carcinoma (OSCC) accounts for ∼90% of all oral cancer,8,9 these two terms tend to be used interchangeably. OSCC, which originates as an epithelial dysplasia (from a histopathologic perspective), generally develops from precursor lesions termed as oral potentially malignant disorders (OPMDs).10,11 Nevertheless, it is not inevitable that all OPMDs, even the most commonly encountered such as oral lichen planus, leukoplakia, and erythroplakia,11 result in the subsequent development of malignancies.10,12 Diagnosing OPMDs as definable diseases is also challenging due to the numerous varieties, various forms, and overlapping features. However, studies10,13,14 have found that when an OPMD changes to a nonhomogeneous presentation, it is more likely to be considered as an adverse progression, in other words, nonhomogeneous lesions have a greater risk of malignant transformation as against homogeneous lesions. Hence, compared with defined diagnosis, distinguishing the potential malignant characteristics of OPMD is a greater concern. In summary, an ideal clinical prediction method should be employed to diagnose OSCC early and in particular to assess malignancy at the OPMD stage. Currently, conventional oral examination (COE) consisting of visual and tactile assessment (followed by tissue biopsy if there are any suspicious findings) is the most routine procedure in the management of oral cancer and precursor disease. However, one limitation for COE is that several features of oral cancer may appear benign and even mimic aphthous ulcers, and they are too clinically heterogeneous and subtle for general dentists to distinguish. Second, because of its invasive nature and sampling bias that can lead to underdiagnosis or misdiagnosis, biopsy is often not ideal as a screening tool.15 Furthermore, although specialists can recognize most of the characteristics that differentiate benign and cancerous lesions, the number of specialists and health resources are limited and concentrated across regions, causing a large part of the oral cancer burden to fall on low-resource communities. Therefore, the idea of establishing a cost-effective screening strategy as an adjunctive aid to the current procedures is gaining widespread popularity.16 Recently, deep learning techniques have exhibited a comparative advantage over feature-based methods in medical image analysis. A variety of studies17,18 showed that deep learning algorithms are able to surpass the performance of human experts in many disease recognition scenarios. In oral cancer diagnosis, deep learning methods also showed promising results for automatic analysis of pathology, confocal laser endomicroscopy (CLE) images, and fluorescence images. For example, Kumar et al.19 proposed a two-stage method that used a segmentation network and a random forest tree classifier to identify different stages of oral cancer in histological images. Aubreville et al.20 tested the deep convolutional neural network for OSCC diagnosis on CLE images, and the results showed that it outperformed the feature-based classification methods. Song et al.21,22 developed mobile connected devices to acquire fluorescence oral images and used them to identify oral disease. However, these methods all require expensive devices or a specifically designed screening platform, which are not accessible to everyone. In other words, patients are still required to go a professional clinic to receive disease diagnosis. With the rapid development of both imaging and sensing technologies in camera systems, the ubiquity of smartphones is equipped with higher quality, low-noise, and faster camera modules. Smartphone-based white light inspection methods23–25 are good solutions for acquiring oral images. Camalan et al.23 used a CNN-based network for classifying white light images as normal or suspicious; however, the patient sample is very limited: with only 54 cases. To build a reliable system, Welikala et al.24 collected more clinically labeled data for network training and evaluation, achieving a sensitivity of 52.13% and a specificity of 49.11% on multiclass classification task. However, we found that the clinical imaging capturing from hand-held smartphone cameras may exhibit a large variability, leading to poor diagnostic performance of the detection algorithm. For example, from a computer vision point of view, the shape and size of imaging lesions depend on the fields-of-view and focal distance, respectively. Unfortunately, a limited amount of research focuses on this problem. To address these challenges, we aim to explore reliable and robust smartphone-based white light image approaches, including an image-capturing method, resampling method, and high performance of CNN model, for oral disease recognition. Our main contributions are summarized as follows. 1. We propose a simple yet effective image-capturing method for consistent lesion position and focal distance over different images. The method allows direct focus on discriminative parts for disease recognition, without utilizing any region proposal methods,24,25 or relying on bounding box annotation24 by oral specialists. 2. We present a resampling method to alleviate the effect of image variability that introduced by the hand-held smartphone camera; at the same time, it can remedy the class imbalance problem. 3. We use one of the latest proposed convolutional neural networks (HRNet) for oral disease classification and achieve better results than common classification models. Analysis of the model’s performance on our collected images shows that common imaging pattern on a smartphone is a valuable approach for the early diagnosis. 2. Materials and Methods 2.1. Image-Capturing Method The focal length of the main camera in a smartphone is commonly short, for example, the iPhone 12 has a wide-angle (only 26 mm) camera. The focal length number tells us how much of the scene is captured in the picture, and the lower the number is, the wider the view is. Since most lesions are relatively small, we cannot capture the photograph where the lesion occupies most areas of the image. This means that the captured image may have many irrelevant backgrounds. In addition, even with the same lesion, the size of the imaging lesion may vary with different distances between the camera and lesion or using different cameras with different focal lengths. Thus these would introduce the large variability to the system’s performance going forward, and we need to find a method to reduce this considerable variability. Although identifying oral disease is very difficult for the nonmedical expert, locating the position of oral lesions is relatively easy because the visual appearance of normal and diseased tissue is significantly different. We use the camera grid to assist in locating the lesion in the center of image and to keep each area of lesion in the images neither too small nor too big. Thus it is possible to use the fixed region of interest (ROI) method to crop the discriminative parts and filter the irrelevant backgrounds, without utilizing any region proposal methods or relying on any manually cropped methods. This particular positioning of the main object in an image is helpful to improve the performance of CNN for image recognition. We use the native phone’s camera app to capture the oral cavity image. As shown in Fig. 1, the camera grid helps us to see if the lesion is properly placed at the center for optimal balance in the shot. The operation can be easily done by a person using a hand-held smartphone camera. The basic steps are as follows. Open in a separate window Fig. 1 Illustration of data acquisition. The aspect ratio is set 4:3; the image shows a lesion at the center of the region.
Published: 2021

16. Deep feature loss to denoise OCT images using deep neural networks

Author: Di Xiao, Jason Kugelman, David Alonso-Caneiro, Maryam Mehdizadeh, Cara MacNish, and Mohammed Bennamoun
Subjects: Paper, Computer science, Noise reduction, Feature extraction, Biomedical Engineering, Image processing, Signal-To-Noise Ratio, Retina, Imaging, Biomaterials, Speckle pattern, convolutional neural networks, Image Processing, Computer-Assisted, image enhancement, optical coherence tomography, business.industry, Deep learning, Pattern recognition, Speckle noise, Atomic and Molecular Physics, and Optics, image processing, Electronic, Optical and Magnetic Materials, Feature (computer vision), Neural Networks, Computer, Noise (video), Artificial intelligence, speckle, business, Tomography, Optical Coherence
Abstract: Significance: Speckle noise is an inherent limitation of optical coherence tomography (OCT) images that makes clinical interpretation challenging. The recent emergence of deep learning could offer a reliable method to reduce noise in OCT images. Aim: We sought to investigate the use of deep features (VGG) to limit the effect of blurriness and increase perceptual sharpness and to evaluate its impact on the performance of OCT image denoising (DnCNN). Approach: Fifty-one macula-centered OCT pairs were used in training of the network. Another set of 20 OCT pair was used for testing. The DnCNN model was cascaded with a VGG network that acted as a perceptual loss function instead of the traditional losses of L1 and L2. The VGG network remains fixed during the training process. We focused on the individual layers of the VGG-16 network to decipher the contribution of each distinctive layer as a loss function to produce denoised OCT images that were perceptually sharp and that preserved the faint features (retinal layer boundaries) essential for interpretation. The peak signal-to-noise ratio (PSNR), edge-preserving index, and no-reference image sharpness/blurriness [perceptual sharpness index (PSI), just noticeable blur (JNB), and spectral and spatial sharpness measure (S3)] metrics were used to compare deep feature losses with the traditional losses. Results: The deep feature loss produced images with high perceptual sharpness measures at the cost of less smoothness (PSNR) in OCT images. The deep feature loss outperformed the traditional losses (L1 and L2) for all of the evaluation metrics except for PSNR. The PSI, S3, and JNB estimates of deep feature loss performance were 0.31, 0.30, and 16.53, respectively. For L1 and L2 losses performance, the PSI, S3, and JNB were 0.21 and 0.21, 0.17 and 0.16, and 14.46 and 14.34, respectively. Conclusions: We demonstrate the potential of deep feature loss in denoising OCT images. Our preliminary findings suggest research directions for further investigation.
Published: 2021

17. Deep CNN models for predicting COVID-19 in CT and x-ray images

Author: Ahmad Chaddad, Lama Hassan, and Christian Desrosiers
Subjects: Paper, Deep cnn, medicine.diagnostic_test, Receiver operating characteristic, Coronavirus disease 2019 (COVID-19), Coronavirus disease 2019, business.industry, Darknet, convolutional neural network, Computed tomography, New infection, Pattern recognition, transfer learning, Convolutional neural network, Computer-Aided Diagnosis, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, radiomics, 030220 oncology & carcinogenesis, medicine, X ray image, Radiology, Nuclear Medicine and imaging, Artificial intelligence, business
Abstract: Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model’s ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.
Published: 2021

18. Human embryonic stem cell classification: random network with autoencoded feature extractor

Author: Nikki Jo-Hao Weng, Bir Bhanu, Hengyue Liu, Rajkumar Theagarajan, Prue Talbot, and Benjamin X. Guan
Subjects: Paper, Neural Networks, Computer science, Human Embryonic Stem Cells, cell classification, Biomedical Engineering, Special Series on Artificial Intelligence and Machine Learning in Biomedical Optics, Video microscopy, human embryonic stem cell, Optical Physics, Regenerative Medicine, Data modeling, Biomaterials, Computer, Opthalmology and Optometry, Feature (machine learning), Humans, Stem Cell Research - Embryonic - Human, Random graph, Contextual image classification, business.industry, Deep learning, Pattern recognition, Optics, Image segmentation, bioinformatics, Stem Cell Research, phase contrast videos, Autoencoder, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Neural Networks, Computer, Artificial intelligence, Generic health relevance, business
Abstract: SignificanceAutomated understanding of human embryonic stem cell (hESC) videos is essential for the quantified analysis and classification of various states of hESCs and their health for diverse applications in regenerative medicine.AimThis paper aims to develop an ensemble method and bagging of deep learning classifiers as a model for hESC classification on a video dataset collected using a phase contrast microscope.ApproachThe paper describes a deep learning-based random network (RandNet) with an autoencoded feature extractor for the classification of hESCs into six different classes, namely, (1)cell clusters, (2)debris, (3)unattached cells, (4)attached cells, (5)dynamically blebbing cells, and (6)apoptotically blebbing cells. The approach uses unlabeled data to pre-train the autoencoder network and fine-tunes it using the available annotated data.ResultsThe proposed approach achieves a classification accuracy of 97.23 ± 0.94 % and outperforms the state-of-the-art methods. Additionally, the approach has a very low training cost compared with the other deep-learning-based approaches, and it can be used as a tool for annotating new videos, saving enormous hours of manual labor.ConclusionsRandNet is an efficient and effective method that uses a combination of subnetworks trained using both labeled and unlabeled data to classify hESC images.
Published: 2021

19. Aberration-free digital holographic phase imaging using the derivative-based principal component analysis

Author: Sheng Xiao, Xiaomin Lai, Kaihua Wei, Shanhui Fan, and Chen Xu
Subjects: Paper, Computer science, Biomedical Engineering, Holography, Phase (waves), Coma (optics), Derivative, digital holographic microscopy, 01 natural sciences, law.invention, 010309 optics, Biomaterials, aberration compensation, law, 0103 physical sciences, Humans, Segmentation, Microscopy, Principal Component Analysis, business.industry, Astigmatism, Pattern recognition, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Principal component analysis, phase imaging, Digital holographic microscopy, Artificial intelligence, business, Digital holography, Algorithms
Abstract: Significance: Digital holographic microscopy is widely used to get the quantitative phase information of transparent cells. Aim: However, the sample phase is superimposed with aberrations. To quantify the phase information, aberrations need to be fully compensated. Approach: We propose a technique to obtain aberration-free phase imaging, using the derivative-based principal component analysis (dPCA). Results: With dPCA, almost all aberrations can be extracted and compensated without requirements on background segmentation, making it efficient and convenient. Conclusions: It solves the problem that the conventional principal component analysis (PCA) algorithm cannot compensate the common but intricate higher order cross-term aberrations, such as astigmatism and coma. Moreover, the dPCA strategy proposed here is not only suitable for aberration compensation but also applicable for other cases where there exist cross-terms that cannot be analyzed with the PCA algorithm.
Published: 2021

20. Signal regression in frequency-domain diffuse optical tomography to remove superficial signal contamination

Author: Joshua Deepak Veesa and Hamid Dehghani
Subjects: Paper, high density diffuse optical tomography, superficial signal contamination, Neuroscience (miscellaneous), Phase (waves), frequency domain, 01 natural sciences, Signal, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, Linear regression, Radiology, Nuclear Medicine and imaging, Detection theory, signal regression, Physics, Radiological and Ultrasound Technology, business.industry, Pattern recognition, Research Papers, Diffuse optical imaging, Regression, Intensity (physics), Frequency domain, functional near-infrared imaging, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract: Significance: Signal contamination is a major hurdle in functional near-infrared spectroscopy (fNIRS) of the human head as the NIR signal is contaminated with the changes corresponding to superficial tissue, therefore occluding the functional information originating from the cerebral region. For continuous wave, this is generally handled through linear regression of the shortest source-detector (SD) distance intensity measurement from all of the signals. Although phase measurements utilizing frequency domain (FD) provide deeper tissue sampling, the use of the shortest SD distance phase measurement for regression of superficial signal contamination can lead to misleading results, therefore suppressing cortical signals. Aim: An approach for FD fNIRS that utilizes a short-separation intensity signal directly to regress both intensity and phase measurements, providing a better regression of superficial signal contamination from both data-types, is proposed. Approach: Simulated data from realistic models of the human head are used, and signal regression using both intensity and phase-based components of the FD fNIRS is evaluated. Results: Intensity-based phase regression achieves a suppression of superficial signal contamination by 68% whereas phase-based phase regression is only by 13%. Phase-based phase regression is also shown to generate false-positive signals from the cortex, which are not desirable. Conclusions: Intensity-based phase regression provides a better methodology for minimizing superficial signal contamination in FD fNIRS.
Published: 2021

21. High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks

Author: Seonghwan Park, Faliu Yi, and Inkyu Moon
Subjects: Paper, Erythrocytes, Computer science, Holography, Biomedical Engineering, Image processing, digital holographic microscopy, 01 natural sciences, Convolutional neural network, Imaging, law.invention, cell counting, 010309 optics, Biomaterials, Biological specimen, law, 0103 physical sciences, optical information processing, business.industry, Deep learning, deep learning, Pattern recognition, Image segmentation, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, holography application, red blood cell analysis, Digital holographic microscopy, Neural Networks, Computer, Artificial intelligence, business, Algorithms, Digital holography
Abstract: Significance: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable. Aim: Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future. Approach: The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting. Results: The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200 μm×200 μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate. Conclusions: High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.
Published: 2021

22. Research on the combination of color channels in heart rate measurement based on photoplethysmography imaging

Author: JongSong Ryu, Shifeng Yan, Qingyue Chen, Shili Liang, SinIl Pak, and SunChol Hong
Subjects: Paper, Computer science, sRGB, Biomedical Engineering, YCbCr, Signal-To-Noise Ratio, Color space, Signal, photoplethysmography imaging, Biomaterials, Signal-to-noise ratio, Interference (communication), Heart Rate, Humans, Photoplethysmography, General, Signal processing, business.industry, Signal Processing, Computer-Assisted, Pattern recognition, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, prior-knowledge-based method, combination of color channels, RGB color model, Artificial intelligence, plane-orthogonal-to-skin based method, business, Algorithms
Abstract: Significance: The measurement of human vital signs based on photoplethysmography imaging (PPGI) can be severely affected by the interference of various factors in the measurement process; therefore, a lot of complex signal processing techniques are used to remove the influence of the interference. Aim: We comprehensively analyze several methods for color channel combination in the color spaces currently used in PPGI and determine the combination method that can improve the quality of the pulse signal, which results in a modified plane-orthogonal-to-skin based method (POS). Approach: Based on the analysis of the previous studies, 13 methods for color channel combination in the different color spaces, which can be seen as having potential abilities in measuring vital signs, were compared by employing the average value of signal-to-noise ratio (SNR) and the box-plot in the public databases UBFC-RPPG and PURE. In addition, the pulse signal was extracted through the dual-color space transformation (sRGB → intensity normalized RGB → YCbCr) and fine-tuning on the CbCr plane. Results: Among the 13 methods for color channel combination, the signal extracted by the Cb+Cr combination in the YCbCr color space includes the most pulse information. Furthermore, the average SNR of the modified POS for all the used databases is improved by 69.3% compared to POS. Conclusions: The methods using prior knowledge are not only simple to calculate but can significantly increase the SNR, which will provide a great help in the practical use of vital sign measurements based on PPGI.
Published: 2021

23. Non-destructive DNA recovery from handwritten documents using a dry vacuum technique

Author: Patrick McLaughlin, Christian Hopkins, Mechthild Prinz, and Eliot Springer
Subjects: Paper, Paper document, Vacuum, Computer science, Dna recovery, Suction, 01 natural sciences, Pathology and Forensic Medicine, Specimen Handling, 03 medical and health sciences, 0302 clinical medicine, Fingerprint, Non destructive, DNA database, Genetics, Humans, 030216 legal & forensic medicine, Dermatoglyphics, Collection methods, business.industry, 010401 analytical chemistry, Pattern recognition, DNA, DNA Fingerprinting, 0104 chemical sciences, DNA profiling, Str typing, Artificial intelligence, business, Microsatellite Repeats
Abstract: Investigations of many crimes such as robberies, kidnappings, and terrorism are often associated with the recovery of a paper document which has been written by the perpetrator. Paper can provide a variety of forensic evidence such as DNA, latent fingermarks, and indented writing. The focus of this study was DNA recovery from handwritten notes through a vacuum suction device while preserving the other evidence types and the integrity of the document. Copy paper was used to create handwritten documents and sheets with deliberate fingerprints, and indentations. The homemade vacuum device consists of a glass pipette blocked with a moistened swab and attached to a vacuum source. The method collected sufficient DNA amounts for DNA typing analysis with 80% of the 11 copy paper samples tested giving probative DNA profiles with five being eligible for DNA database entry. DNA recovery was also tested on other commonly encountered paper types. DNA quantities would have been sufficient for STR typing for approximately 50% of manila envelopes and notebook paper samples, but not for magazine pages and bank deposit slips. Deliberate sebaceous and eccrine latent fingermarks placed onto copy paper and developed with magnetic fingerprint developer or 1,2 indanedione were not affected by the vacuum swabbing technique. Simulated robbery notes with indented writing and processed using an Electrostatic Detection Apparatus (ESDA) demonstrated no interference through the DNA collection. This vacuum-based collection method enables laboratories to reverse the current questioned document workflow and start with DNA collection.
Published: 2021

24. Comparison of short-channel separation and spatial domain filtering for removal of non-neural components in functional near-infrared spectroscopy signals

Author: Xian Z. Zhang, Swethasri Dravida, Joy Hirsch, Ilias Tachtsidis, Richard N. Aslin, J. Adam Noah, Tatsuya Suzuki, and Courtney DiCocco
Subjects: Paper, Computer science, Neuroscience (miscellaneous), 01 natural sciences, Signal, spatial filter, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, medicine, functional near-infrared spectroscopy, Radiology, Nuclear Medicine and imaging, short channel, Radiological and Ultrasound Technology, medicine.diagnostic_test, Spatial filter, business.industry, Pattern recognition, Filter (signal processing), Research Papers, Regression, Principal component analysis, Functional near-infrared spectroscopy, systemic artifact, Artificial intelligence, Functional magnetic resonance imaging, business, 030217 neurology & neurosurgery, Communication channel
Abstract: Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.
Published: 2021

25. Separation of color channels from conventional colonoscopy images improves deep neural network detection of polyps

Author: Andrew M. Blakely, Thomas Lu, Trilokesh D. Kidambi, Kurt Melstrom, Marta Invernizzi, Lily L. Lai, Kevin Yu, and James Lin
Subjects: Paper, Computer science, Feature extraction, Biomedical Engineering, Colonic Polyps, Colonoscopy, colorectal cancer, 01 natural sciences, Colonoscopes, 010309 optics, Biomaterials, Narrow Band Imaging, 0103 physical sciences, medicine, Humans, Diagnosis, Computer-Assisted, General, Contextual image classification, medicine.diagnostic_test, color channel separation, Color image, business.industry, Deep learning, polyp discrimination, deep learning, Pattern recognition, Image segmentation, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, RGB color model, Neural Networks, Computer, Artificial intelligence, artificial intelligence algorithms, business, narrow-band imaging
Abstract: Significance: Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination. Aim: To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE). Approach: Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps. Results: The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images. Conclusions: The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use.
Published: 2021

26. The ATLAS fast TracKer system

Author: Aad, Georges, Abbott, Braden Keim, Abreu, Henso, Arcangeletti, Chiara, Gutierrez Zagazeta, Luis Felipe, Gutschow, Christian, Guyot, Claude, Gwenlan, Claire, Gwilliam, Carl, Haaland, Even Simonsen, Haas, Andy, Haas, Andrew, Haber, Carl, Hadavand, Haleh, Arce, Ayana Tamu, Hadef, Asma, Haleem, Mahsana, Haley, Joseph, Hall, Jack Joseph, Halladjian, Garabed, Hallewell, Gregory, Hamano, Kenji, Hamdaoui, Hassane, Hamer, Matthias, Hamity, Guillermo Nicolas, Arguin, Jean-francois, Han, Kunlin, Han, Liangliang, Han, Liang, Han, Shuo, Han, Yi Fei, Hanagaki, Kazunori, Hance, Mike, Hank, Michael Donald, Hankache, Robert, Hansen, Eva Brottmann, Argyropoulos, Spyros, Hansen, Jorgen Beck, Hansen, Dines, Hansen, Maike Christina, Hansen, Peter, Hanson, Emily Claire, Hara, Kazuhiko, Harenberg, Torsten, Harkusha, Siarhei, Harrison, Paul Fraser, Hartman, Nicole Michelle, Arling, Jan-hendrik, Hartmann, Nikolai, Hasegawa, Yoji, Hasib, Ahmed, Hasley, Lloyd Alan, Hassani, Samira, Haug, Sigve, Hauser, Reiner, Havranek, Miroslav, Hawkes, Chris, Hawkings, Richard, Armbruster, Aaron James, Hayashida, Shota, Hayden, Daniel, Hayes, Christopher Robyn, Hayes, Robin, Hays, Chris, Hays, Jonathan, Hayward, Helen, Haywood, Stephen, He, Fudong, He, Yunjian, Armstrong, Alexander, Heath, Matthew Peter, Hedberg, Vincent, Heggelund, Andreas Lokken, Hehir, Natasha, Heidegger, Constantin, Heidegger, Kim Katrin, Heidorn, William Dale, Heilman, Jesse Alan, Heim, Sarah, Heim, Timon, Arnaez, Olivier, Heinemann, Beate, Heinlein, James Geddy, Heinrich, Jochen Jens, Heinrich, Lukas Alexander, Hejbal, Jiri, Helary, Louis, Held, Alexander, Hellesund, Simen, Helling, Cole Michael, Hellman, Sten, Arnold, Hannah, Helsens, Clement, Henderson, Robert, Henkelmann, Lars, Henriques Correia, Ana Maria, Herde, Hannah Elizabeth, Hernandez Jimenez, Yesenia, Herr, Holger Arnold, Herrmann, Maximilian Georg, Herrmann, Tim, Herten, Gregor, Arrubarrena Tame, Zulit Paola, Hertenberger, Ralf, Hervas, Luis, Hessey, Nigel, Hibi, Hiroaki, Higashino, Satoshi, Higon-rodriguez, Emilio, Hildebrand, Kevin, Hill, John, Hill, Kurt Keys, Hiller, Karl Heinz, Abulaiti, Yiming, Artoni, Giacomo, Hu, Yi Fan, Hiller, Karlheinz, Hillier, Stephen, Hils, Maximilian, Hinchliffe, Ian, Hinterkeuser, Florian, Hirose, Minoru, Hirose, Shigeki, Hirschbuehl, Dominic, Hiti, Bojan, Asada, Haruka, Hladik, Ondrej, Hobbs, John David, Hobincu, Radu, Tal Hod, Noam, Hodgkinson, Mark, Hoecker, Andreas, Hohn, David, Hohov, Dmytro, Holm, Tanja, Holmes, Tova, Asai, Kanae, Holzbock, Michael, Hommels, Bart, Hong, Tae Min, Honig, Jan Cedric, Honle, Andreas, Hooberman, Benjamin Henry, Hopkins, Walter, Horii, Yasuyuki, Horn, Philipp, Horyn, Lesya Anna, Asai, Shoji, Hou, Suen, Howarth, James William, Hoya, Joaquin, Hrabovsky, Miroslav, Hrynevich, Aliaksei, Hrynova, Tetiana, Hsu, Pai-hsien, Hsu, Shih-chieh, Hu, Qipeng, Hu, Shuyang, Asawatavonvanich, Thanawat, Iakovidis, Georgios, Hu, Yifan, Huang, Danping, Huang, Xiaozhong, Huang, Yicong, Huang, Yanping, Hubacek, Zdenek, Hubaut, Fabrice, Hubner, Michael, Huegging, Fabian, Asbah, Nedaa Alexandra, Huffman, Todd Brian, Huhtinen, Mika, Hulsken, Raphael, Hunter, Robert Francis, Husejko, Michal, Huseynov, Nazim, Huston, Joey, Huth, John, Hyneman, Rachel Jordan, Hyrych, Sofiia, Asimakopoulou, Eleni Myrto, Iacobucci, Giuseppe, Iakovidis, George, Ibragimov, Iskander, Iconomidou-fayard, Lydia, Iengo, Paolo, Ignazzi, Rosanna, Iguchi, Ryunosuke, Iizawa, Tomoya, Ikegami, Yoichi, Ilic, Nikolina, Asquith, Lily, Imam, Hajar, Introzzi, Gianluca, Iodice, Mauro, Iordanidou, Kalliopi, Ippolito, Valerio, Isacson, Max Fredrik, Ishino, Masaya, Islam, Wasikul, Issever, Cigdem, Istin, Serhat, Assahsah, Jihad, Ito, Hiroki, Iturbe, Julia, Iuppa, Roberto, Ivina, Anna, Izen, Joseph Michael, Izzo, Vincenzo, Jacka, Petr, Jackson, Paul, Jacobs, Ruth Magdalena, Jaeger, Benjamin Paul, Assamagan, Ketevi Adikle, Jakel, Gunnar, Jakobi, Katharina Bianca, Jakobs, Karl, Jakoubek, Tomas, Jamieson, Jonathan, Janas, Krzysztof, 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Serbanp, Proudfoot, James, Przybycien, Mariusz, Pudzha, Dennis, Puri, Akshat, Puzo, Patrick Michel, Behr, Janna Katharina, Pyatiizbyantseva, Diana, Qian, Jianming, Qin, Quake, Quadt, Arnulf, Queitsch-maitland, Michaela, Rabanal Bolanos, Gabriel, Racko, Michal, Ragusa, Francesco, Rahal, Ghita, Raine, Johnny, Beisiegel, Florian, Rajagopalan, Srini, Ramirez Morales, Andres, Ran, Kunlin, Rassloff, Damir Fabrice, Rauch, Daniel, Rauscher, Felix, Rave, Stefan, Ravina, Baptiste, Ravinovich, Ilia, Raymond, Michel, Belfkir, Mohamed, Read, Alexander Lincoln, Readioff, Nathan Peter, Reale, Marilea, Rebuzzi, Daniela, Redlinger, George, Reeves, Kendall, Reikher, David, Reiss, Andreas Dominik, Rej, Amartya, Rembser, Christoph, Bella, Gideon, Renardi, Alessia, Renda, Michele, Rendel, Marian Benedikt, Rennie, Adam, Resconi, Silvia, Resseguie, Elodie Deborah, Rettie, Sebastien, Reynolds, Bryan, Reynolds, Elliot, Rezanova, Olga, Bellagamba, Lorenzo, Reznicek, Pavel, Ricci, Ester, Richter, Robert, 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R. D., Schaile, Dorothee, Schamberger Jr, Robert Dean, Schanet, Eric, Scharf, Christian, Scharmberg, Nicolas, Berge, David, Chtcheguelski, Valery, Scheirich, Daniel, Schenck, Ferdinand, Schernau, Michael, Schiavi, Carlo, Schildgen, Lara Katharina, Schillaci, Zachary Michael, Schioppa, Enrico Junior, Schioppa, Marco, Schleicher, Katharina, Bergeaas Kuutmann, Elin, Schlenker, Stefan, Schmidt-sommerfeld, Korbinian Ralf, Schmieden, Kristof, Schmitt, Christian, Schmitt, Stefan, Schoeffel, Laurent Olivier, Schoening, Andre, Scholer, Patrick, Schopf, Elisabeth, Schott, Matthias, Berger, Nicolas, Schouwenberg, Jeroen, Schovancova, Jaroslava, Schramm, Steven, Schroeder, Frederic, Schulte, Alexandra, Schultz-coulon, Hans-christian, Schumacher, Markus, Schumm, Bruce Andrew, Schune, Philippe, Schwartzman, Ariel Gustavo, Bergmann, Benedikt Ludwig, Schwarz, Thomas Andrew, Schwemling, Philippe, Schwienhorst, Reinhard, Sciandra, Andrea, Sciolla, Gabriella, Scuri, Fabrizio, Scutti, Federico, Scyboz, Ludovic Michel, Sebastiani, Cristiano, Sedlaczek, Kevin, Bergsten, Laura Jean, Seema, Pienpen, Seidel, Sally, Seiden, Abraham, Seidlitz, Blair Daniel, Seiss, Todd Michael, Seitz, Claudia, Seixas, Jose, Sekhniaidze, Givi, Sekula, Stephen Jacob, Semprini Cesari, Nicola, Beringer, Juerg, Sen, Sourav, Serfon, Cedric, Serin, Laurent, Serkin, Leonid, Sessa, Marco, Severini, Horst, Sevova, Stanislava, Sforza, Federico, Sfyrla, Anna, Shabalina, Elizaveta, Berlendis, Simon, Shahinian, Jeffrey David, Shaikh, Nabila Wahab, Shatalov, Pavel, Shojaii, Jafar, Sijacki, Djordje, Soukharev, Andrey, Shahinian, Jeff, Ahlgren, Nabila Wahab, Shaked Renous, Dan, Shan, Lianyou, Bernardi, Gregorio, Shang, Ruo-yu, Shapiro, Marjorie, Sharma, Abhishek, Shatalov, Sppavel, Shaw, Kate, Shaw, Savanna, Shehade, Mahran, Shen, Yu-ting, Sherwood, Peter, Bernius, Catrin, Shi, Liaoshan, Shimmin, Chase Owen, Shimogama, Yoshihiro, Shimojima, Makoto, Shinner, James David, Shipsey, Ian, Shirabe, Shohei, Shiyakova, Mariya, Shlomi, Jonathan, Shmeleva, Alevtina, Agapopoulou, Christina, Bernlochner, Florian Urs, Shochet, Mel, Shojaii, Seyed Ruhollah, Shope, David Richard, Shrestha, Suyog, Shrif, Esra Mohammed, Shroff, Maheyer Jamshed, Shulga, Evgeny, Sicho, Petr, Sickles, Anne Marie, Sideras Haddad, Elias, Berry, Tracey, Sidiropoulou, Ourania, Sidoti, Antonio, Siegert, Frank, Solodkov, Alexander, Sijacki, Dorde, Silva Oliveira, Marcos Vinicius, Silverstein, Samuel, Simion, Stefan, Simoniello, Rosa, Simpson-allsop, Cameron James, Berta, Peter, Simsek, Sinem, Sinervo, Pekka, Sinetckii, Viktor, Singh, Sundeep, Sinha, Sukanya, Sioli, Maximiliano, Siral, Ismet, Sivoklokov, Serguei, Sjoelin, Joergen, Skaf, Ali, Berthold, Anne-sophie, Skorda, Eleni, Skubic, Patrick, Slawinska, Magdalena, Sliwa, Krzysztof Jan, Smakhtin, Vladimir, Smart, Ben Harry, Smiesko, Juraj, Smirnov, Nikita, Smirnov, Serge, Smirnov, Yury, Bertram, Iain, Smirnova, Lidia, Smirnova, Oxana, Smith, Emily Ann, Smith, Hayden Alexander, Smizanska, Maria, Smolek, Karel, Smykiewicz, Andrzej, Snesarev, Andrei, Snoek, Hella, Snyder, Ian Michael, Bessidskaia Bylund, Olga, Snyder, Scott, Sobie, Randall, Soffer, Abner, Soloshenko, Alexei, Sopio, Alexander Linus, Sobie, Randy, Soffer, Abi, Sogaard, Andreas, Sohns, Fabian, Solans Sanchez, Carlos, Bethke, Siegfried, Soldatov, Evgeny, Soldevila Serrano, Urmila, Solodkov, Sanya, Soloshenko, Aleksey, Solovyanov, Oleg, Solovyev, Victor, Sommer, Philip, Son, Hyungsuk, Sonay, Anil, Song, Wen Yi, Betti, Alessandra, Sopczak, Andre, Sopio, Alex, Sopkova, Filomena, Sotiropoulou, Calliope-louisa, Sottocornola, Simone, Soualah, Rachik, Sukharev, Andrei, South, David, Stavropoulos, Georgios, Spagnolo, Stefania, Bevan, Adrian, Spalla, Margherita, Spangenberg, Martin, Spano, Francesco, Sperlich, Dennis, Spieker, Thomas Malte, Spigo, Giancarlo, Spina, Mario, Spiteri, Dwayne Patrick, Spousta, Martin, Strauss, Michael, Bhatta, Somadutta, Stabile, Alberto, Stamas, Brianna, Stamen, Rainer, Stamenkovic, Marko, Stampekis, Alexios, Stanecka, Ewa, Stanislaus, Beojan, Stanitzki, Marcel, Stankaityte, Migle, Stapf, Birgit Sylvia, Agaras, Merve Nazlim, Bhattacharya, Deb Sankar, Starchenko, Jenya, Stark, Giordon Holtsberg, Stark, Jan, Staroba, Pavel, Starovoitov, Pavel, Staerz, Steffen, Staszewski, Rafal, Stavropoulos, George, Steinberg, Peter Alan, Steinhebel, Amanda, Bhattarai, Prajita, Stelzer, Bernd, Stelzer, Joerg, Stelzer-chilton, Oliver, Stenzel, Hasko, Stevenson, Thomas James, Stewart, Graeme A, Stockton, Mark, Stoicea, Gabriel, Stolarski, Marcin, Stonjek, Stefan, Bhopatkar, Vallary Shashikant, Straessner, Arno, Strandberg, Jonas, Strandberg, Sara, Strauss, Mike, Strebler, Thomas, Strizenec, Pavol, Strohmer, Raimund, Strom, David, Stroynowski, Ryszard, Strubig, Antonia, Bi, Runyu, Stucci, Stefania Antonia, Stugu, Bjarne, Stupak, John, Styles, Nicholas, Su, Dong, Su, Wanyun, Su, Xiaowen, Suarez, Nicholas Bruno, Sulin, Vladimir, Sullivan, Matthew James, Bianchi, Riccardo Maria, Sultan, D. 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S., Sultanov, Salekh, Sumida, Toshi, Sun, Siyuan, Sun, Xiaohu, Suster, Carl Joseph Edmund, Sutton, Mark, Svatos, Michal, Swiatlowski, Maximilian J, Swift, Stewart Patrick, Biebel, Otmar, Swirski, Thorben, Sydorenko, Alexander, Sykora, Ivan, Sykora, Martin, Sykora, Tomas, Ta, Duc Bao, Tackmann, Kerstin, Kinghorn-taenzer, Joseph Peter, Taffard, Anyes, Tafirout, Reda, Biedermann, Dustin, Tagiev, Emin, Taibah, Reem Hani M, Takashima, Ryuichi, Takeda, Kosuke, Takeshita, Toru, Takeva, Emily Petrova, Takubo, Yosuke, Talby, Mossadek, Talyshev, Alexei, Tam, Kai Chung, Bielski, Rafal, Tamir, Nadav Michael, Tanaka, Junichi, Tanaka, Reisaburo, Tokar, Stanislav, Tsukerman, Ilya, Tsybychev, Dmitri, Tuts, Michael, Vacek, Vaclav, Tanaka, Rei, Tang, Fukun, Bierwagen, Katharina, Tapia Araya, Sebastian, Tapprogge, Stefan, Tarek, Ahmed, Tarem, Shlomit, Tariq, Khuram, Tarna, Grigore, Vandewall, Evan Richard, Vermeulen, Jos, Tartarelli, Francesco, Tas, Petr, Biesuz, Nicolo Vladi, Tasevsky, Marek, Tassi, 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Zorbas, Theodore, Zou, Rui, Zwalinski, Lukasz, ATLAS Collaboration, Abed Abud, Adam, Ahmadov, Faig, Bortoletto, Daniela, Boscherini, Davide, Fernandez-bosman, Martine, Bossio, Jonathan, Bouaouda, Khalil, Boudreau, Joseph, Bouhova-thacker, Eva, Boumediene, Djamel Eddine, Bouquet, Romain, Bourlis, Georgios, Ahmed, Waleed Syed, Boveia, Antonio, Boyd, Jamie, Boye, Diallo, Boyko, Igor, Bozson, Adam, Bracinik, Juraj, Brahimi, Nihal, Brandt, Gerhard Immanuel, Brandt, Oleg, Braren, Frued Erik, Ai, Xiaocong, Brau, Benjamin Paul, Brau, Jim, Breaden Madden, Will, Brendlinger, Kurt, Schimmel Brener, Roy, Brenner, Lydia, Brenner, Richard, Bressler, Shikma, Brickwedde, Bernard, Briglin, Daniel Lawrence, Aielli, Giulio, Britton, David, Britzger, Daniel Andreas, Brock, Ian, Brock, Raymond, Brooijmans, Gustaaf, Brooks, William King, Brost, Elizabeth, Bruckman De Renstrom, Pawel, Bruers, Ben, Bruncko, Dusan, Akatsuka, Shunichi, Bruni, Alessia, Bruni, Graziano, Bruschi, Marco, Bruscino, Nello, Bryant, Patrick Eugene, Bryngemark, Lene Kristian, Buanes, Trygve, Buat, Quentin, Buchholz, Peter, Buckley, Andy, Akbiyik, Melike, Budagov, Ioulian, Bugge, Magnar Kopangen, Bulekov, Oleg, Bullard, Brendon, Burch, Tyler James, Burdin, Sergey, Burgard, Carsten Daniel, Burger, Angela Maria, Burghgrave, Blake Oliver, Burr, Jon, Akesson, Torsten, Burton, Charles, Burzynski, Jackson Carl, Buescher, Volker, Buschmann, Eric, Bussey, Peter John, Butler, John Mark, Buttar, Craig, Butterworth, Jonathan, Buzykaev, Aleksey, Buttinger, Will, Akilli, Ece, Buxo Vazquez, Carlos Josue, Buzykaev, Alexey, Cabras, Grazia, Cabrera Urban, Susana, Caforio, Davide, Cai, Huacheng, Cairo, Valentina, Cakir, Orhan, Calace, Noemi, Calafiura, Paolo, Akimov, Andrei, Calderini, Giovanni, Calfayan, Philippe, Callea, Giuseppe, Caloba, Luiz, Caltabiano, Alessandro, Calvente Lopez, Sergio, Calvet, David, Calvet, Samuel, Calvet, Thomas Philippe, Calvetti, Milene, Al Khoury, Konie, Camacho Toro, Reina Coromoto, Camarda, Stefano, Camarero Munoz, Daniel, Camarri, Paolo, Camerlingo, Maria Teresa, Cameron, David, Camincher, Clement, Campana, Simone, Campanelli, Mario, Camplani, Alessandra, Abeling, Kira, Alberghi, Gian Luigi, Canale, Vincenzo, Canesse, Auriane, Bret Cano, Marc, Cantero Garcia, Josu, Cao, Yumeng, Capua, Marcella, Cardarelli, Roberto, Cardillo, Fabio, Carducci, Giovandomenico, Carli, Tancredi, Albert, Justin, Carlino, Giampaolo, Carlson, Ben, Carlson, Evan Michael, Carminati, Leonardo, Carney, Rebecca, Caron, Sascha, Carquin Lopez, Edson, Carra, Sonia, Carratta, Giuseppe, Carter, Joseph, Albicocco, Pietro, Carter, Thomas Michael, Casado Lechuga, Pilar, Casha, Albert Francis, Castiglia, Emma Grace, Castillo, Florencia Luciana, Castillo Garcia, Lucia, Castillo Gimenez, Victoria, Castro, Nuno, Catinaccio, Andrea, Catmore, James, Alconada, Josefina, Cattai, Ariella, Cavaliere, Viviana, Cavasinni, Vincenzo, Celebi, Emre, Celli, Federico, Cerny, Karel, Santiago Cerqueira, Augusto, Cerri, Alex, Cerrito, Lucio, Cerutti, Fabio, Alderweireldt, Sara, Cervelli, Alberto, Cetin, Serkant, Chadi, Zakaria, Chakraborty, Dhiman, Chan, Jay, Chan, Terry Ws, Chan, Wai Yuen, Chapman, John Derek, Chargeishvili, Bakar, Charlton, Dave, Aleksa, Martin, Charman, Thomas Paul, Chatterjee, Meghranjana, Chau, Chav Chhiv, Chekanov, Sergei, Chekulaev, Sergey, Shelkov, G., Chen, Boping, Chen, Cheng, Chen, Chunhui, Chen, Huirun, Alexandrov, Igor, Chen, Hucheng, Chen, Jing, Chen, Jue, Chen, Jiayi, Chen, Shion, Chen, Shenjian, Chen, Xin, Chen, Ye, Chen, Yu-heng, Cheng, Hok Chuen, Alexa, Calin, Cheng, Huajie, Cheplakov, Alexander, Cheremushkina, Evgeniya, Cherkaoui El Moursli, Rajaa, Cheu, Elliott, Cheung, Kingman, Chevalerias, Thibault, Chevalier, Laurent, Chiarella, Vitaliano, Chiarelli, Giorgio, Alexopoulos, Theodoros, Chiodini, Gabriele, Chisholm, Andrew Stephen, Chitan, Adrian, Chiu, I-huan, Chiu, Justin, Chizhov, Mihail, Choi, Kyungeon, Chomont, Arthur, Chou, Yuan-tang, Chow, Edwin, Alfonsi, Alice, Christopher, Lawrence Davou, Chu, Ming Chung, Chu, Xiaotong, Chudoba, Jiri, Chwastowski, Janusz, Cieri, Davide, Ciesla, Krzysztof, Cindro, Vladimir, Cioara, Irina Antonela, Ciocio, Alessandra, Abhayasinghe, Deshan Kavishka, Alfonsi, Fabrizio, Cirotto, Francesco, Citraro, Saverio, Citron, Zvi, Citterio, Mauro, Ciubotaru, Dan Andrei, Ciungu, Bianca Monica, Clark, Allan, Clark, Philip, Clawson, Savannah, Clement, Christophe, Alhroob, Muhammad, Clissa, Luca, Coadou, Yann, Cobal, Marina, Coccaro, Andrea, Cochran Jr, James Herbert, Coelho Lopes De Sa, Rafael, Cohen, Hadar, Coimbra, Artur, Cole, Brian, Colijn, Auke-pieter, Ali, Babar, Collot, Johann, Conde Muino, Patricia, Connell, Simon, Connelly, Ian Allan, Conventi, Francesco, Sarkar, Amanda, Cormier, Felix, Corpe, Louie Dartmoor, Corradi, Massimo, Corrigan, Eric Edward, Ali, Shahzad, Corriveau, Francois, Costa Mezquita, Maria Jose, Costanza, Francesco, Costanzo, Davide, Cowan, Glen, Cowley, James William, Crane, Jonathan, Cranmer, Kyle Stuart, Creager, Rachael Ann, Crepe-renaudin, Sabine, Aliev, Malik, Crescioli, Francesco, Cristinziani, Markus, Cristoforetti, Marco, Croft, Vincent Alexander, Crone, Gordon, Crosetti, Giovanni, Cunningham, William Reilly, Crosetti, Nanni, Cueto Gomez, Ana Rosario, Cuhadar Donszelmann, Tulay, Alimonti, Gianluca, Cui, Han, Cukierman, Aviv Ruben, Dandoy, Jeffrey Rogers, Darbo, Giovanni, Cunningham, Liam, Czekierda, Sabina Anna, Czodrowski, Patrick Karl, Czurylo, Marta, Sousa, Mario Jose, Da Fonseca Pinto, Joao Victor, Alison, John, Da Via, Cinzia, Dabrowski, Wladyslaw, Dachs, Florian, Dado, Tomas, Dahbi, Salah-eddine, Dai, Tiesheng, Dallapiccola, Carlo, Dam, Mogens, D'amen, Gabriele, D'amico, Valerio, Allaire, Corentin, Damp, Johannes Frederic, Dandoy, Jeff, Daneri, Maria Florencia, Danninger, Matthias, Dao, Valerio, Darbo, Nanni, Dartsi, Olympia, Dattagupta, Aparajita, D'auria, Saverio, David, Claire, Allbrooke, Benedict, Davidek, Tomas, Davis, Douglas Raymond, Dawson, Ian, De, Kaushik, De Asmundis, Riccardo, De Beurs, Marcus, De Castro, Stefano, De Groot, Nicolo, Vicente, Marcelo, De Jong, Paul, Allport, Philip Patrick, De La Torre Perez, Hector, De Maria, Antonio, De Pedis, Daniele, De Salvo, Alessandro, De Sanctis, Umberto, De Santis, Maurizio, De Santo, Antonella, De Vivie De Regie, Jean-baptiste, Dedovich, Dmitri, Deiana, Allison Mccarn, Abidi, Haider, Aloisio, Alberto, Del Peso, Jose, Delabat Diaz, Yasiel, Delgove, David, Deliot, Frederic, Delitzsch, Chris Malena, Della Pietra, Massimo, Della Volpe, Domenico, Dell'acqua, Andrea, Dell'asta, Lidia, Delmastro, Marco, Alonso, Francisco, Delporte, Charles Eliaz, Delsart, Pierre Antoine, Demers Konezny, Sarah Marie, Demichev, Mikhail, Demontigny, Gabriel, Denisov, Sergey, Denisov, Serguei, D'eramo, Louis, Derendarz, Dominik Karol, Derkaoui, Jamal, Alpigiani, Cristiano, Derue, Frederic, Dervan, Paul, Desch, Klaus, Dette, Karola, Deutsch, Christopher, Deviveiros, Pier-olivier, Di Bello, Francesco Armando, Di Ciaccio, Anna, Di Ciaccio, Lucia, Di Donato, Camilla, Alunno Camelia, Elio, Di Girolamo, Alessandro, Di Gregorio, Giulia, Di Luca, Andrea, Di Micco, Biagio, Di Nardo, Roberto, Di Sipio, Riccardo, Diaconu, Cristinel, De Almeida Dias, Flavia, Vale, Tiago, Diaz Gutierrez, Marco Aurelio, Alvarez Estevez, Manuel, Diaz Capriles, Federico Guillermo, Dickinson, Jennet Elizabeth, Didenko, Mariia, Diehl, Edward, Dietrich, Janet, Diez Cornell, Sergio, Diez Pardos, Carmen, Dimitrievska, Aleksandra, Ding, Wei, Dingfelder, Jochen Christian, Alviggi, Mariagrazia, Dittmeier, Sebastian, Dittus, Fido, Djama, Fares, Djobava, Tamar, Djuvsland, Julia Isabell, Barros do Vale, Maria Aline, Dobre, Monica, Dodsworth, David Michael, Doglioni, Caterina, Dolejsi, Jiri, Do Amaral Coutinho, Yara, Dolezal, Zdenek, Donadelli, Marisilvia, Dong, Binbin, Donini, Julien Noce, D'onofrio, Adelina, D'onofrio, Monica, Dopke, Jens, Doria, Alessandra, Dova, Maria Teresa, Doyle, Tony, Ambler, Alessandro, Drake, Gary, Drechsler, Eric, Dreyer, Etienne, Dreyer, Timo, Drobac, Alec Swenson, Du, Dongshuo, Du Pree, Tristan Arnoldus, Duan, Yanyun, Dubinin, Filipp, Dubovsky, Michal, Ambroz, Luca, Dubreuil, Arnaud, Duchovni, Ehud, Duckeck, Guenter, Ducu, Otilia Anamaria, Duda, Dominik, Dudarev, Alexey, Dudder, Andreas Christian, D'uffizi, Matteo, Duflot, Laurent, Duehrssen-debling, Michael, Amelung, Christoph, Dulsen, Carsten, Dumancic, Mirta, Dumitriu, Ana Elena, Dunford, Monica, Dungs, Sascha, Duperrin, Arnaud, Yildiz, Duran, Dueren, Michael Johannes, Durglishvili, Archil, Dutta, Baishali, Abouzeid, Hass, Amidei, Dante, Duvnjak, Damir, Dyckes, George, Ellis, Nicolas, Dyckes, Ian, Dyndal, Mateusz, Dysch, Samuel Dezso, Dziedzic, Bartosz Sebastian, Eggleston, Michael Glenn, Eifert, Till, Eigen, Gerald, Amor Dos Santos, Susana Patricia, Einsweiler, Kevin Frank, Ekelof, Tord Johan Carl, El Jarrari, Hassnae, Ellajosyula, Venugopal, Ellert, Mattias, Ellinghaus, Frank, Elliot, Alison, Ellis, Nick, Emerman, Alexander, Elmsheuser, Johannes, Amoroso, Simone, Elsing, Markus, Emeliyanov, Dmitry, Ezhilov, Alexey, Emerman, Alex, Enari, Yuji, Erdmann, Johannes, Ereditato, Antonio, Erland, Paula Agnieszka, Errenst, Martin, Escalier, Marc, Amrouche, Cherifa Sabrina, Escobar Ibanez, Carlos, Estrada Pastor, Oscar, Etzion, Erez, Gaspar De Andrade Evans, Guiomar, Evans, Hal, Evans, Meirin Oan, Ezhilov, Aleksei, Fabbri, Federica, Fabbri, Laura, Fabiani, Veronica, Anastopoulos, Christos, Facini, Gabriel, Fakhrutdinov, Rinat, Falciano, Speranza, Falke, Peter Johannes, Falke, Saskia, Faltova, Jana, Fang, Yi, Fang, Yaquan, Fanourakis, Georgios, Fanti, Marcello, Andari, Nansi, Faraj, Mohammed, Farbin, Amir, Farilla, Addolorata, Farilla, Ada, Farina, Edoardo Maria, Farooque, Trisha, Farrington, Sinead, Farthouat, Philippe, Fassi, Farida, Fassnacht, Patrick, Andeen, Timothy Robert, Fassouliotis, Dimitris, Faucci Giannelli, Michele, Fawcett, William James, Fayard, Louis, Fedin, Oleg, Fehr, Armin, Feickert, Matthew Carl, Feligioni, Lorenzo, Fell, Alix, Fell, Alexandra, Anders, John Kenneth, Feng, Cunfeng, Feng, Minyu, Fenton, Michael James, Fenyuk, Alexandre, Ferguson, Sarah Whitney, Ferrando, James Edward, Ferrari, Arnaud, Ferrari, Pamela, Ferrari, Roberto, Ferrer Soria, Antonio, Anderson, John Thomas, Ferrere, Didier, Ferretti, Claudio, Fiedler, Frank, Filipcic, Andrej, Filthaut, Frank, Finelli, Kevin Daniel, Castro Nunes Fiolhais, Miguel, Fiorini, Luca, Fischer, Florian, Fischer, Julian, Andrean, Stefio Yosse, Fisher, Wade Cameron, Fitschen, Tobias, Fleck, Ivor, Fleischmann, Philipp, Flick, Tobias, Flierl, Bernhard Matthias, Flores, Lucas Macrorie, Flores Castillo, Luis Roberto, Follega, Francesco Maria, Fomin, Nikolai, Abraham, Nicola Louise, Andreazza, Attilio, Foo, Joel Hengwei, Forcolin, Giulio Tiziano, Forland, Blake Christopher, Formica, Andrea, Forster, Fabian Alexander, Forti, Alessandra, Fortin, Etienne Marie, Foti, Maria Giovanna, Fournier, Daniel, Fox, Harald, Andrei, Victor, Francavilla, Paolo, Francescato, Simone, Franchini, Matteo, Franchino, Silvia, Francis, David, Franco, Luca, Franconi, Laura, Franklin, Melissa, Frattari, Guglielmo, Freeman, Patrick Moriishi, Anelli, Christopher Ryan, Freund, Benjamin, Spolidoro Freund, Werner, Freundlich, Elena Murielle, Frizzell, Dylan Cooper, Froidevaux, Daniel, Frost, James, Fujimoto, Minori, Fullana Torregrosa, Esteban, Fusayasu, Takahiro, Fuster Verdu, Juan, Angelidakis, Stylianos, Gabrielli, Alessandro, Gabrielli, Andrea, Gadow, Paul Philipp, Gagliardi, Guido, Gagnon, Louis-guillaume, Gallardo, Gabriel, Gallas, Elizabeth, Gallop, Bruce Joseph, Gamboa Goni, Rodrigo, Gan, KK, Angerami, Aaron, Garcia Pascual, Juan Antonio, Gan, Kock Kiam, Ganguly, Sanmay, Gao, Jun, Gao, Yanyan, Gao, Yongsheng, Garay Walls, Francisca, Garcia, Carmen, Garcia Navarro, Jose Enrique, Garcia, Juanan, Anisenkov, Alexey, Garcia-sciveres, Maurice, Gardner Jr, Robert William, Garelli, Nicoletta, Gargiulo, Simona, Garner, Christopher Andrew, Garonne, Vincent, Gasiorowski, Sean Joseph, Do Nascimento Gaspar, Philipp, Gatta, Maurizio, Gaudio, Gabriella, Annovi, Alberto, Gauzzi, Paolo, Gavrilenko, Igor, Gavrilyuk, Alexander, Gavriliuk, Aleksandr, Gay, Colin Warren, Gaycken, Goetz, Gazis, Evangelos, Geanta, Andrei Alexandru, Gee, Carolyn, Gee, Norman, Antel, Claire, Geisen, Jannik, Geisen, Marc, Gemme, Claudia, Genest, Marie-helene, Geng, Cong, Gentile, Simonetta, Gentsos, Christos, George, Simon, Geralis, Theodoros, Gerlach, Lino Oscar, Anthony, Matthew Thomas, Gessinger-befurt, Paul, Gessner, Gregor, Ghasemi Bostanabad, Meisam, Ghneimat, Mazuza, Ghosh, Aishik, Ghosh, Anindya, Giacobbe, Benedetto, Giagu, Stefano, Giangiacomi, Nico, Giannetti, Paola, Antipov, Egor, Giannini, Antonio, Giannini, Giulia, Gibson, Stephen, Gignac, Matthew, Gil, Damian Tomasz, Gilbert, Benjamin Jacob, Gillberg, Dag, Gilles, Geoffrey, Gillwald, Nils, Gingrich, Douglas, Abramowicz, Halina, Antonelli, Mario, Giordani, MarioPaolo, Gingrich, Doug, Giordani, Mapo, Giraud, Pierre-francois, Giugliarelli, Gilberto, Giugni, Danilo, Giuli, Francesco, Gkaitatzis, Stamatios, Gkialas, Ioannis, Gkougkousis, Evangelos, Antrim, Daniel Joseph, Gkougkousis, Vagelis, Gkountoumis, Panagiotis, Gladilin, Leonid, Glasman, Claudia, Glaysher, Paul, Gledhill, Galen Rhodes, Gnesi, Ivan, Goblirsch-kolb, Maximilian Emanuel, Godin, Dominique, Goldfarb, Steven, Anulli, Fabio, Golling, Tobias, Golubkov, Dmitry, Da Silva Gomes, Agostinho, Goncalves Gama, Rafael, Morais Silva Goncalo, Ricardo Jose, Gonella, Giulia, Gonella, Laura, Gongadze, Alexi, Gonnella, Francesco, Gonski, Julia Lynne, Aoki, Masato, Gonzalez De La Hoz, Santiago, Gonzalez Fernandez, Sergio, Gonzalez Lopez, Ricardo, Gonzalez Renteria, Cesar, Gonzalez Suarez, Rebeca, Gonzalez Sevilla, Sergio, Gonzalvo Rodriguez, Galo Rafael, Goossens, Luc, Gorasia, Nandish Arjan, Gorbounov, Petr, Aparisi Pozo, Javier Alberto, Gordon, Howard, Gorini, Benedetto, Gorini, Edoardo, Gorisek, Andrej, Goshaw, Alfred, Grillo, Alexander, Goshaw, Al, Gostkin, Mikhail, Gottardo, Carlo Alberto, Gouighri, Mohamed, Aparo, Marco, Goussiou, Anna, Govender, Nicolin, Goy, Corinne, Grabowska-bold, Iwona, Gramstad, Eirik, Grancagnolo, Sergio, Grandi, Mario, Grachev, Vadim, Gravila, Paul, Gravili, Francesco Giuseppe, Aperio Bella, Ludovica, Gray, Chloe, Gray, Heather, Green, Barry, Grefe, Christian, Gregor, Ingrid, Grenier, Philippe, Grevtsov, Kirill, Grieco, Chiara, Grieser, Nathan Allen, Grillo, Alex, Aranzabal Barrio, Nordin, Grimm, Kathryn, Grinstein, Sebastian, Grivaz, Jean-francois, Groh, Sabrina, Gross, Eilam, Grosse-knetter, Joern, Grout, Zara Jane, Grud, Christopher Ryan, Grummer, Aidan, Grundy, James Cameron, Araujo Ferraz, Victor, Guan, Liang, Guan, Wen, Gubbels, Chris, Guenther, Jaroslav, Gubbels, Christopher, Guerrero Rojas, Jesus, Guescini, Francesco, Guest, Dan, Guest, Daniel, Gugel, Ralf, Pereira, Rodrigo, Guida, Alessandro, Guillemin, Thibault, Guindon, Stefan, Guo, Jun, Guo, Ziyu, Gupta, Ruchi, Gurbuz, Saime, Gustavino, Giuliano, Guth, Manuel, Gutierrez, Phillip, Aad, G, Bachas, K, Chiodini, G, Gorini, E, Gravili, F, Longo, L, Primavera, M, Reale, M, Schioppa, E, Spagnolo, S, Ventura, A, ATLAS Collaboration, And, Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre de Calcul de l'IN2P3 (CC-IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), ATLAS, Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), De Castro , S., Fabbri , L., Gabrielli , A., Rinaldi , L., Semprini Cesari , N., Sioli , M., Villa , M., Zoccoli , A., Bellagamba , L., Boscherini , D., Bruni , A., Bruni , G., Giacobbe , B., Polini , A., Sbarra , C., Sidoti , A., Franchini , M., Romano , M., Valentinetti , S., Alberghi , G. L., Lasagni Manghi , F, Massa , L., Vittori , C., Alfonsi , F, Cabras , G., Cervelli , A., Negrini , M., Todome , K., Carratta , G., Cavalli , N., Clissa , L., ATLAS collaboration, Aad, G., Abbott, B., Abbott, D. C., Abud, A. A., Abeling, K., Abhayasinghe, D. K., Abidi, S. H., Abouzeid, O. S., Abraham, N. L., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Achkar, B., Adam, L., Bourdarios, C. A., Adamczyk, L., Adamek, L., Adelman, J., Adiguzel, A., Adorni, S., Adye, T., Affolder, A. A., Afik, Y., Agapopoulou, C., Agaras, M. N., Aggarwal, A., Agheorghiesei, C., Aguilar-Saavedra, J. A., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ai, X., Aielli, G., Akatsuka, S., Akbiyik, M., Akesson, T. P. A., Akilli, E., Akimov, A. V., Al Khoury, K., Alberghi, G. L., Albert, J., Albicocco, P., Verzini, M. J. A., Alderweireldt, S., Aleksa, M., Aleksandrov, I. 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[0000-0003-1988-8401], Yoshihara, K. [0000-0002-3656-2326], Young, C.J.S. [0000-0001-5858-6639], Young, C. [0000-0003-3268-3486], Yuan, R. [0000-0002-8452-0315], Yue, X. [0000-0001-6956-3205], Zaazoua, M. [0000-0002-4105-2988], Zabinski, B. [0000-0001-5626-0993], Zacharis, G. [0000-0002-3156-4453], Zaffaroni, E. [0000-0003-1714-9218], Zahreddine, J. [0000-0002-6932-2804], Zaitsev, A.M. [0000-0002-4961-8368], Zakareishvili, T. [0000-0001-7909-4772], Zakharchuk, N. [0000-0002-4963-8836], Zambito, S. [0000-0002-4499-2545], Zanzi, D. [0000-0002-1222-7937], Zeißner, S.V. [0000-0002-9037-2152], Zeitnitz, C. [0000-0003-2280-8636], Zemaityte, G. [0000-0001-6331-3272], Zeng, J.C. [0000-0002-2029-2659], Zenin, O. [0000-0002-5447-1989], Ženiš, T. [0000-0001-8265-6916], Zenz, S. [0000-0002-9720-1794], Zerradi, S. [0000-0001-9101-3226], Zerwas, D. [0000-0002-4198-3029], Zgubič, M. [0000-0002-5110-5959], Zhang, B. [0000-0002-9726-6707], Zhang, D.F. [0000-0001-7335-4983], Zhang, G. [0000-0002-5706-7180], Zhang, J. [0000-0002-9907-838X], Zhang, K. [0000-0002-9778-9209], Zhang, L. [0000-0002-9336-9338], Zhang, L. [0000-0001-5241-6559], Zhang, M. [0000-0001-8659-5727], Zhang, R. [0000-0002-8265-474X], Zhang, X. [0000-0003-4731-0754], Zhang, X. [0000-0003-4341-1603], Zhang, Y. [0000-0002-4554-2554], Zhang, Z. [0000-0002-7853-9079], Zhao, P. [0000-0003-0054-8749], Zhao, Y. [0000-0003-0494-6728], Zhao, Z. [0000-0001-6758-3974], Zhemchugov, A. [0000-0002-3360-4965], Zheng, Z. [0000-0002-8323-7753], Zhong, D. [0000-0001-9377-650X], Zhou, C. [0000-0001-5904-7258], Zhou, H. [0000-0002-7986-9045], Zhou, M. [0000-0001-7223-8403], Zhou, N. [0000-0002-1775-2511], Zhu, C.G. [0000-0001-8015-3901], Zhu, C. [0000-0002-5918-9050], Zhu, H.L. [0000-0001-8479-1345], Zhu, H. [0000-0001-8066-7048], Zhu, J. [0000-0002-5278-2855], Zhu, Y. [0000-0002-7306-1053], Zhuang, X. [0000-0003-0996-3279], Zhukov, K. [0000-0003-2468-9634], Zhulanov, V. [0000-0002-0306-9199], Zieminska, D. [0000-0002-6311-7420], Zimine, N.I. [0000-0003-0277-4870], Zimmermann, S. [0000-0002-1529-8925], Živković, L. [0000-0003-4236-8930], Zoccoli, A. [0000-0002-0993-6185], Zoch, K. [0000-0003-2138-6187], Zorbas, T.G. [0000-0003-2073-4901], Zou, R. [0000-0002-0542-1264], Zwalinski, L. [0000-0002-9397-2313], Apollo - University of Cambridge Repository, Współautorami artykułu są członkowie ATLAS Collaboration w liczbie 2890, Aloisio, Alberto, Alviggi, Mariagrazia, Canale, Vincenzo, Della Pietra, Massimo, Merola, Leonardo, Massarotti, Paolo, Conventi, Francesco, Rossi, Elvira, Carlino, Giampaolo, de Asmundis, Riccardo, Di Donato, Camilla, Doria, Alessandra, Iengo, Paolo, Izzo, Vincenzo, Perrella, Sabrina, Sekhniaidze, Givi, Cirotto, Francesco, Lavorgna, Marco, Giannini, Antonio, and Atlas, Collaboration
Subjects: Kjerne- og elementærpartikkelfysikk: 431 [VDP], Physics - Instrumentation and Detectors, Computer science, data acquisition, Tracking (particle physics), 01 natural sciences, Online farms and online filtering, High Energy Physics - Experiment, Subatomär fysik, High Energy Physics - Experiment (hep-ex), Data acquisition, Software, cluster finding, Subatomic Physics, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], hardware, Calibration and fitting methods, Cluster finding, Modular electronics, Pattern recognition, Trigger concepts and systems (hardware and software), Detectors and Experimental Techniques, Instrumentation, Mathematical Physics, FPGA, Settore FIS/01, Large Hadron Collider, Pattern recognition, cluster finding, calibration and fitting methods, track data analysis, Detector, integrated circuit, microstrip [semiconductor detector], Modular electronic, Instrumentation and Detectors (physics.ins-det), calibration and fitting methods, ATLAS, medicine.anatomical_structure, Nuclear and elementary particle physics: 431 [VDP], Pattern recognition (psychology), design [electronics], semiconductor detector: microstrip, LHC, Computer hardware, performance, Particle Physics - Experiment, 530 Physics, FOS: Physical sciences, LHC, ATLAS, High Energy Physics, Accelerator Physics and Instrumentation, programming, Calibration and fitting method, semiconductor detector: pixel, Atlas (anatomy), 0103 physical sciences, medicine, High Energy Physics, ddc:610, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], 010306 general physics, Field-programmable gate array, numerical calculations, pixel [semiconductor detector], 010308 nuclear & particles physics, business.industry, paper, bibliography, Acceleratorfysik och instrumentering, trigger, LHC, Particle Tracking, High-level Trigger, Particle Tracking, efficiency, High-level Trigger, data management, business, Hadron-hadron collisions, electronics: design
Abstract: We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerba¼an; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia and NRC KI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada, CRC and IVADO, Canada; Be¼ing Municipal Science & Technology Commission, China; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO TEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF(Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [52]., The ATLAS Fast TracKer (FTK) was designed to provide full tracking for the ATLAS high-level trigger by using pattern recognition based on Associative Memory (AM) chips and fitting in high-speed field programmable gate arrays. The tracks found by the FTK are based on inputs from all modules of the pixel and silicon microstrip trackers. The as-built FTK system and components are described, as is the online software used to control them while running in the ATLAS data acquisition system. Also described is the simulation of the FTK hardware and the optimization of the AM pattern banks. An optimization for long-lived particles with large impact parameter values is included. A test of the FTK system with the data playback facility that allowed the FTK to be commissioned during the shutdown between Run 2 and Run 3 of the LHC is reported. The resulting tracks from part of the FTK system covering a limited η-ϕ region of the detector are compared with the output from the FTK simulation. It is shown that FTK performance is in good agreement with the simulation., ANPCyT, Argentina, YerPhI, Armenia, ARC, Australia, BMWFW and FWF, Austria, ANAS, Azerbaijan, SSTC, Belarus, CNPq and FAPESP, Brazil, NSERC, NRC and CFI, Canada, CERN, ANID, Chile, CAS, MOST and NSFC, China, COLCIENCIAS, Colombia, MSMT CR, MPO CR and VSC CR, Czech Republic, DNRF and DNSRC, Denmark, IN2P3-CNRS and CEA-DRF/IRFU, France, SRNSFG, Georgia, BMBF, HGF and MPG, Germany, GSRT, Greece, RGC and Hong Kong SAR, China, ISF and Benoziyo Center, Israel, INFN, Italy, MEXT and JSPS, Japan, CNRST, Morocco, NWO, Netherlands, RCN, Norway, MNiSW and NCN, Poland, FCT, Portugal, MNE/IFA, Romania, JINR, MES of Russia and NRC KI, Russian Federation, MESTD, Serbia, MSSR, Slovakia, ARRS and MIZŠ, Slovenia, DST/NRF, South Africa, MICINN, Spain, SRC and Wallenberg Foundation, Sweden, SERI, SNSF and Cantons of Bern and Geneva, Switzerland, MOST, Taiwan, TAEK, Turkey, STFC, United Kingdom, DOE and NSF, United States of America, BCKDF, CANARIE, Compute Canada, CRC and IVADO, Canada, Bejiing Municipal Science & Technology Commission, China, COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union, Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France, DFG and AvH Foundation, Germany, Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece, BSF-NSF and GIF, Israel, La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain, Göran Gustafssons Stiftelse, Sweden, The Royal Society and Leverhulme Trust, United Kingdom
Published: 2021

27. ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos

Author: Adrien Chan-Hon-Tong, Guillaume Vaudaux-Ruth, Catherine Achard, Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Perception, Interaction, Robotique sociales (PIROS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université, DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau], ONERA-Université Paris Saclay (COmUE), DTIS, ONERA, Université Paris Saclay [Palaiseau], ONERA-Université Paris-Saclay, Vaudaux-Ruth, Guillaume, Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), and Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Machine Learning, Index Terms-Class, Computer science, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), IEEEtran, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0202 electrical engineering, electronic engineering, information engineering, Reinforcement learning, typesetting, Reinforcement learning algorithm, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Ground truth, business.industry, paper, template, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Spotting, style, Artificial Intelligence (cs.AI), Action (philosophy), Pattern recognition (psychology), Key frame, 020201 artificial intelligence & image processing, Artificial intelligence, L A T E X, business
Abstract: Summarizing video content is an important task in many applications. This task can be defined as the computation of the ordered list of actions present in a video. Such a list could be extracted using action detection algorithms. However, it is not necessary to determine the temporal boundaries of actions to know their existence. Moreover, localizing precise boundaries usually requires dense video analysis to be effective. In this work, we propose to directly compute this ordered list by sparsely browsing the video and selecting one frame per action instance, task known as action spotting in literature. To do this, we propose ActionSpotter, a spotting algorithm that takes advantage of Deep Reinforcement Learning to efficiently spot actions while adapting its video browsing speed, without additional supervision. Experiments performed on datasets THUMOS14 and ActivityNet show that our framework outperforms state of the art detection methods. In particular, the spotting mean Average Precision on THUMOS14 is significantly improved from 59.7% to 65.6% while skipping 23% of video.
Published: 2021

28. Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography

Author: Jana M. Kainerstorfer, Pulkit Grover, Theodore J. Huppert, and Jiaming Cao
Subjects: Paper, Resolution enhancement technologies, Computer science, neurovascular coupling, Neuroscience (miscellaneous), Iterative reconstruction, Electroencephalography, 01 natural sciences, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, medicine, functional near-infrared spectroscopy, Radiology, Nuclear Medicine and imaging, Image resolution, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Brain atlas, Pattern recognition, image reconstruction, Research Papers, Diffuse optical imaging, Temporal resolution, Functional near-infrared spectroscopy, diffuse optical tomography, Artificial intelligence, business, electroencephalography, 030217 neurology & neurosurgery
Abstract: Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.
Published: 2021

29. Semi-Automated 3D Segmentation of Pelvic Region Bones in CT Volumes for the Annotation of Machine Learning Datasets

Author: Alexandr Malusek, Michael Sandborg, José Carlos González Sánchez, Julius Jeuthe, Maria Magnusson, and Åsa Carlsson Tedgren
Subjects: Paper, Similarity (geometry), Computer science, medicine.medical_treatment, Brachytherapy, Dice, Pelvis, 030218 nuclear medicine & medical imaging, Machine Learning, 03 medical and health sciences, Annotation, 0302 clinical medicine, Hounsfield scale, Health Sciences, Image Processing, Computer-Assisted, medicine, Radiology, Nuclear Medicine and imaging, Pelvic Bones, Ground truth, AcademicSubjects/SCI00180, Radiation, Radiological and Ultrasound Technology, Artificial neural network, business.industry, Visual comparison, Environmental and Occupational Health, Public Health, Environmental and Occupational Health, Pattern recognition, General Medicine, Hälsovetenskaper, Radiology Nuclear Medicine and imaging, 030220 oncology & carcinogenesis, Neural Networks, Computer, Artificial intelligence, Public Health, Tomography, X-Ray Computed, business, Algorithms
Abstract: Automatic segmentation of bones in computed tomography (CT) images is used for instance in beam hardening correction algorithms where it improves the accuracy of resulting CT numbers. Of special interest are pelvic bones, which—because of their strong attenuation—affect the accuracy of brachytherapy in this region. This work evaluated the performance of the JJ2016 algorithm with the performance of MK2014v2 and JS2018 algorithms; all these algorithms were developed by authors. Visual comparison, and, in the latter case, also Dice similarity coefficients derived from the ground truth were used. It was found that the 3D-based JJ2016 performed better than the 2D-based MK2014v2, mainly because of the more accurate hole filling that benefitted from information in adjacent slices. The neural network-based JS2018 outperformed both traditional algorithms. It was, however, limited to the resolution of 1283 owing to the limited amount of memory in the graphical processing unit (GPU). Funding: VetenskapsradetSwedish Research Council [VR-NT 2016-05033]
Published: 2021

30. COVID-19 detection and heatmap generation in chest x-ray images

Author: Thanongchai Siriapisith, Pairash Saiviroonporn, Worapan Kusakunniran, Krittanat Sutassananon, Trongtum Tongdee, Sarattha Karnjanapreechakorn, and Punyanuch Borwarnginn
Subjects: Paper, 2019-20 coronavirus outbreak, heatmap, Coronavirus disease 2019 (COVID-19), lung detection, Image Processing, chest x-ray, ResNet, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Region of interest, Medicine, Radiology, Nuclear Medicine and imaging, Sensitivity (control systems), Confidence score, business.industry, COVID-19, Pattern recognition, Radiology Nuclear Medicine and imaging, 030220 oncology & carcinogenesis, Classification result, X ray image, Artificial intelligence, business, Large size
Abstract: Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 × 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.
Published: 2021
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31. Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

Author: Ashis Kumar Dhara, Mariëlle J. A. Jansen, Nick A. Weaver, Josien P. W. Pluim, Robin Strand, Hugo J. Kuijf, Geert Jan Biessels, Medical Image Analysis, and EAISI Health
Subjects: Paper, Image Processing, convolutional neural network, Convolutional neural network, 030218 nuclear medicine & medical imaging, Lesion, 03 medical and health sciences, 0302 clinical medicine, patient-specific, Neuroimaging, Medical imaging, magnetic resonance imaging, Medicine, Radiology, Nuclear Medicine and imaging, Segmentation, medicine.diagnostic_test, business.industry, Medicinsk bildbehandling, Pattern recognition, Magnetic resonance imaging, Image segmentation, Hyperintensity, Medical Image Processing, 030220 oncology & carcinogenesis, Radiologi och bildbehandling, Artificial intelligence, medicine.symptom, business, Radiology, Nuclear Medicine and Medical Imaging
Abstract: Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient. Approach: A pretrained CNN can be updated with a patient’s previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH). Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87. Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient’s previously acquired imaging.
Published: 2020

32. Front Cover: Printed 384‐Well Microtiter Plate on Paper for Fluorescent Chemosensor Arrays in Food Analysis (Chem. Asian J. 16/2022).

Author: Lyu, Xiaojun, Sasaki, Yui, Ohshiro, Kohei, Tang, Wei, Yuan, Yousi, and Minami, Tsuyoshi
Subjects: *MICROPLATES, *FOOD chemistry, *PATTERN recognition systems, *DIGITAL cameras
Abstract: Indeed, the printed 384-well microtiter paper-based fluorescent chemosensor array system combined with imaging analysis and pattern recognition techniques successfully not only categorized saccharides and sulfur-containing amino acids but also classified mono- and disaccharide groups. Paper, sensors, pattern recognition, self-assembly, food analysis Keywords: paper; sensors; pattern recognition; self-assembly; food analysis EN paper sensors pattern recognition self-assembly food analysis 1 1 1 08/19/22 20220815 NES 220815 B A facile paper-based device b embedded with self-assembled fluorescent chemosensors displayed a fingerprint-like response pattern for targets, the image of which was rapidly captured by a portable digital camera. [Extracted from the article]
Published: 2022
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33. Applications of compressive sensing in spatial frequency domain imaging

Author: Hamid Dehghani, Abigail M. Spear, Alexander Bentley, Ben O. L. Mellors, and Christopher R. Howle
Subjects: Paper, Computer science, compressive sensing, Biomedical Engineering, Special Series on Artificial Intelligence and Machine Learning in Biomedical Optics, Iterative reconstruction, Physical Phenomena, Biomaterials, Data acquisition, Image Processing, Computer-Assisted, Image restoration, Signal processing, Phantoms, Imaging, business.industry, Optical Imaging, Hyperspectral imaging, Pattern recognition, Data Compression, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Data set, spatial frequency domain imaging, Compressed sensing, data reduction, Artificial intelligence, business, Data reduction
Abstract: Significance: Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time. Aim: Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy. Approach: cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated. Results: Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of
Published: 2020

34. Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network

Author: Yang-Hsien Lin, Kung-Bin Sung, and Ken Y.-K. Liao
Subjects: Paper, thalassemia, Erythrocytes, quantitative phase imaging, Computer science, Feature extraction, Biomedical Engineering, Holography, digital holographic microscopy, red blood cell, 01 natural sciences, Convolutional neural network, Phase image, 010309 optics, Biomaterials, 0103 physical sciences, Microscopy, business.industry, Deep learning, Pattern recognition, Image segmentation, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Characterization (materials science), mask region-based convolutional neural network, Erythrocyte Count, Digital holographic microscopy, Artificial intelligence, Neural Networks, Computer, business, Digital holography
Abstract: Significance: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable. Aim: An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization. Approach: Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images. Results: The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed. Conclusions: The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.
Published: 2020

35. Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours

Author: Benjamin Haas, Angelo Genghi, Annika Hänsch, Benjamin Geisler, Jan Schreier, Jan Klein, Jan Hendrik Moltz, Christiane Engel, Tomasz Morgas, and Publica
Subjects: Paper, radiotherapy planning, genetic structures, hippocampus, Image Processing, Image registration, Convolutional neural network, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Medical imaging, Medicine, Radiology, Nuclear Medicine and imaging, Segmentation, Rigid transformation, Contouring, medicine.diagnostic_test, business.industry, segmentation, deep learning, Magnetic resonance imaging, Pattern recognition, Image segmentation, 030220 oncology & carcinogenesis, Artificial intelligence, business, training data quality
Abstract: Purpose: Hippocampus contouring for radiotherapy planning is performed on MR image data due to poor anatomical visibility on computed tomography (CT) data. Deep learning methods for direct CT hippocampus auto-segmentation exist, but use MR-based training contours. We investigate if these can be replaced by CT-based contours without loss in segmentation performance. This would remove the MR not only from inference but also from training. Approach: The hippocampus was contoured by medical experts on MR and CT data of 45 patients. Convolutional neural networks (CNNs) for hippocampus segmentation on CT were trained on CT-based or propagated MR-based contours. In both cases, their predictions were evaluated against the MR-based contours considered as the ground truth. Performance was measured using several metrics, including Dice score, surface distances, and contour Dice score. Bayesian dropout was used to estimate model uncertainty. Results: CNNs trained on propagated MR contours (median Dice 0.67) significantly outperform those trained on CT contours (0.59) and also experts contouring manually on CT (0.59). Differences between the latter two are not significant. Training on MR contours results in lower model uncertainty than training on CT contours. All contouring methods (manual or CNN) on CT perform significantly worse than a CNN segmenting the hippocampus directly on MR (median Dice 0.76). Additional data augmentation by rigid transformations improves the quantitative results but the difference remains significant. Conclusions: CT-based training contours for CT hippocampus segmentation cannot replace propagated MR-based contours without significant loss in performance. However, if MR-based contours are used, the resulting segmentations outperform experts in contouring the hippocampus on CT.
Published: 2020

36. Stochastic models for objects and images in oncology and virology: application to PI3K-Akt-mTOR signaling and COVID-19 disease

Author: Luca Caucci and Harrison H. Barrett
Subjects: Paper, Property (programming), Stochastic modelling, coronavirus, Probability density function, computer.software_genre, 030218 nuclear medicine & medical imaging, Image (mathematics), 03 medical and health sciences, 0302 clinical medicine, Voxel, Medicine, Radiology, Nuclear Medicine and imaging, stochastic models, mammalian target of rapamycin signaling, Characteristic function (convex analysis), lipid rafts, Pixel, Stochastic process, business.industry, Biomedical Applications in Molecular, Structural, and Functional Imaging, Pattern recognition, Radiology Nuclear Medicine and imaging, 030220 oncology & carcinogenesis, characteristic functional, Artificial intelligence, business, computer
Abstract: Purpose: The goal of this research is to develop innovative methods of acquiring simultaneous multidimensional molecular images of several different physiological random processes (PRPs) that might all be active in a particular disease such as COVID-19. Approach: Our study is part of an ongoing effort at the University of Arizona to derive biologically accurate yet mathematically tractable models of the objects of interest in molecular imaging and of the images they produce. In both cases, the models are fully stochastic, in the sense that they provide ways to estimate any estimable property of the object or image. The mathematical tool we use for images is the characteristic function, which can be calculated if the multivariate probability density function for the image data is known. For objects, which are functions of continuous variables rather than discrete pixels or voxels, the characteristic function becomes infinite dimensional, and we refer to it as the characteristic functional. Results: Several innovative mathematical results are derived, in particular for simultaneous imaging of multiple PRPs. Then the application of these methods to cancers that disrupt the mammalian target of rapamycin signaling pathway and to COVID-19 are discussed qualitatively. One reason for choosing these two problems is that they both involve lipid rafts. Conclusions: We found that it was necessary to employ a new algorithm for energy estimation to do simultaneous single-photon emission computerized tomography imaging of a large number of different tracers. With this caveat, however, we expect to be able to acquire and analyze an unprecedented amount of molecular imaging data for an individual COVID patient.
Published: 2020

37. Combinatorial active contour bilateral filter for ultrasound image segmentation

Author: Anan Nugroho, Hanung Adi Nugroho, Risanuri Hidayat, Johan Debayle, Department of Electrical & Information Engineering, Universitas Gadjah Mada, Department of Electrical Engineering, Universitas Negeri Semarang (UNNES), Département Procédés de Mise en oeuvre des Milieux Granulaires (PMMG-ENSMSE), Centre Sciences des Processus Industriels et Naturels (SPIN-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire Georges Friedel (LGF-ENSMSE), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), and Universitas Negeri Semarang
Subjects: Paper, active contour, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, CAD, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Sørensen–Dice coefficient, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Medicine, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Radiology, Nuclear Medicine and imaging, Segmentation, bilateral filter, Active contour model, ultrasound, business.industry, Pattern recognition, Speckle noise, Image segmentation, radiology, 3. Good health, Computer-aided diagnosis, 030220 oncology & carcinogenesis, computer-aided diagnosis, Bilateral filter, Artificial intelligence, speckle, Ultrasonic Imaging and Tomography, business
Abstract: International audience; Purpose: Utilization of computer-aided diagnosis (CAD) on radiological ultrasound (US) imaging has increased tremendously. The prominent CAD applications are found in breast and thyroid cancer investigation. To make appropriate clinical recommendations, it is important to accurately segment the cancerous object called a lesion. Segmentation is a crucial step but undoubtedly a challenging problem due to various perturbations, e.g., speckle noise, intensity inhomogeneity, and low contrast.Approach: We present a combinatorial framework for US image segmentation using a bilateral filter (BF) and hybrid region-edge-based active contour (AC) model. The BF is adopted to smooth images while preserving edges. Then the hybrid model of region and edge-based AC is applied along the scales in a global-to-local manner to capture the lesion areas. The framework was tested in segmenting 258 US images of breast and thyroid, which were validated by manual ground truths.Results: The proposed framework is accessed quantitatively based on the overlapping values of the Dice coefficient, which reaches 90.05 ± 5.81 % . The evaluation with and without the BF shows that the enhancement procedure improves the framework well.Conclusions: The high performance of the proposed method in our experimental results indicates its potential for practical implementations in CAD radiological US systems.
Published: 2020

38. Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Author: Michael A. Vogelbaum, Sandra K. Johnston, Norbert G. Campeau, Konstantinos Kamnitsas, Greta B. Liebo, Jeffrey S. Ross, Joseph Ross Mitchell, Christopher H. Hunt, Jared T. Verdoorn, Kyle W. Singleton, Jerrold L. Boxerman, Joseph M. Hoxworth, Theodore J. Passe, Carrie M. Carr, Prasanna Vibhute, John Arrington, Dana E. Rollison, Ameet C. Patel, Kathleen M. Egan, Laurence J. Eckel, Cassandra R. Rickertsen, Brian W. Chong, Alex A. Nagelschneider, Kamala Clark-Swanson, Scott Whitmire, Kent D. Nelson, Karl N. Krecke, John D. Port, Kristin R. Swanson, Ben Glocker, Christopher P. Wood, Alice Patton, Sara Ranjbar, and Leland S. Hu
Subjects: Paper, validation, observer studies, Artificial neural network, business.industry, Image Perception, Observer Performance, and Technology Assessment, Technician, Deep learning, segmentation, deep learning, Pattern recognition, Image segmentation, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Test case, Neuroimaging, Sørensen–Dice coefficient, 030220 oncology & carcinogenesis, brain tumors, Medicine, Radiology, Nuclear Medicine and imaging, Segmentation, Artificial intelligence, business
Abstract: Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p
Published: 2020

39. Grading hypoxic-ischemic encephalopathy in neonatal EEG with convolutional neural networks and quadratic time-frequency distributions

Author: Sean Mathieson, John M. O'Toole, Geraldine B. Boylan, Sumit A. Raurale, Gordon Lightbody, and William P. Marnane
Subjects: Paper, Computer science, 0206 medical engineering, Biomedical Engineering, convolutional neural network, 02 engineering and technology, Electroencephalography, Convolutional neural network, Convolution, Machine Learning, time-frequency distribution, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, medicine, Oversampling, Humans, hypoxic-ischemic encephalopathy, medicine.diagnostic_test, business.industry, Infant, Newborn, Pattern recognition, 020601 biomedical engineering, Confidence interval, Feature (computer vision), Hypoxia-Ischemia, Brain, Artificial intelligence, Neural Networks, Computer, Frequency distribution, business, 030217 neurology & neurosurgery, Kappa, Algorithms
Abstract: Objective. To develop an automated system to classify the severity of hypoxic-ischaemic encephalopathy injury (HIE) in neonates from the background electroencephalogram (EEG). Approach. By combining a quadratic time–frequency distribution (TFD) with a convolutional neural network, we develop a system that classifies 4 EEG grades of HIE. The network learns directly from the two-dimensional TFD through 3 independent layers with convolution in the time, frequency, and time–frequency directions. Computationally efficient algorithms make it feasible to transform each 5 min epoch to the time–frequency domain by controlling for oversampling to reduce both computation and computer memory. The system is developed on EEG recordings from 54 neonates. Then the system is validated on a large unseen dataset of 338 h of EEG recordings from 91 neonates obtained across multiple international centres. Main results. The proposed EEG HIE-grading system achieves a leave-one-subject-out testing accuracy of 88.9% and kappa of 0.84 on the development dataset. Accuracy for the large unseen test dataset is 69.5% (95% confidence interval, CI: 65.3%–73.6%) and kappa of 0.54, which is a significant ( P < 0.001 ) improvement over a state-of-the-art feature-based method with an accuracy of 56.8% (95% CI: 51.4%–61.7%) and kappa of 0.39. Performance of the proposed system was unaffected when the number of channels in testing was reduced from 8 to 2—accuracy for the large validation dataset remained at 69.5% (95% CI: 65.5%–74.0%). Significance. The proposed system outperforms the state-of-the-art machine learning algorithms for EEG grade classification on a large multi-centre unseen dataset, indicating the potential to assist clinical decision making for neonates with HIE.
Published: 2020

40. Photoacoustic image improvement based on a combination of sparse coding and filtering

Author: Yan Yan, Hossein Ghadiri, Alireza Ahmadian, Mohammad Mehrmohammadi, Maryam Basij, Parastoo Farnia, Saeedeh Navaei Lavasani, and Ebrahim Najafzadeh
Subjects: Paper, signal denoising, sparse coding, Swine, Computer science, Image quality, Noise reduction, Biomedical Engineering, Signal-To-Noise Ratio, 01 natural sciences, Signal, Imaging phantom, Imaging, 010309 optics, Biomaterials, Signal-to-noise ratio, 0103 physical sciences, Animals, Computer Simulation, Detection theory, Phantoms, Imaging, business.industry, Noise (signal processing), Spectrum Analysis, Pattern recognition, filtering, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, total variation, photoacoustic imaging, Artificial intelligence, business, Algorithms, Image compression
Abstract: Significance: Photoacoustic imaging (PAI) has been greatly developed in a broad range of diagnostic applications. The efficiency of light to sound conversion in PAI is limited by the ubiquitous noise arising from the tissue background, leading to a low signal-to-noise ratio (SNR), and thus a poor quality of images. Frame averaging has been widely used to reduce the noise; however, it compromises the temporal resolution of PAI. Aim: We propose an approach for photoacoustic (PA) signal denoising based on a combination of low-pass filtering and sparse coding (LPFSC). Approach: LPFSC method is based on the fact that PA signal can be modeled as the sum of low frequency and sparse components, which allows for the reduction of noise levels using a hybrid alternating direction method of multipliers in an optimization process. Results: LPFSC method was evaluated using in-silico and experimental phantoms. The results show a 26% improvement in the peak SNR of PA signal compared to the averaging method for in-silico data. On average, LPFSC method offers a 63% improvement in the image contrast-to-noise ratio and a 33% improvement in the structural similarity index compared to the averaging method for objects located at three different depths, ranging from 10 to 20 mm, in a porcine tissue phantom. Conclusions: The proposed method is an effective tool for PA signal denoising, whereas it ultimately improves the quality of reconstructed images, especially at higher depths, without limiting the image acquisition speed.
Published: 2020

41. Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism

Author: Yankui Sun, Xianlin Yao, and Haoran Zhang
Subjects: Paper, genetic structures, Computer science, Feature extraction, Biomedical Engineering, convolutional neural network, transfer learning, 01 natural sciences, Convolutional neural network, Macular Edema, Imaging, 010309 optics, Biomaterials, Retinal Diseases, Optical coherence tomography, 0103 physical sciences, medicine, Humans, optical coherence tomography, Diabetic Retinopathy, Contextual image classification, medicine.diagnostic_test, business.industry, Deep learning, Pattern recognition, eye diseases, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Visualization, Support vector machine, Statistical classification, Neural Networks, Computer, sense organs, Artificial intelligence, attention mechanism, business, Tomography, Optical Coherence, image classification
Abstract: Significance: Automatic and accurate classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images is essential for assisting ophthalmologist in the diagnosis and grading of macular diseases. Therefore, more effective OCT volume classification for automatic recognition of macular diseases is needed. Aim: For OCT volumes in which only OCT volume-level labels are known, OCT volume classifiers based on its global feature and deep learning are designed, validated, and compared with other methods. Approach: We present a general framework to classify OCT volume for automatic recognizing macular diseases. The architecture of the framework consists of three modules: B-scan feature extractor, two-dimensional (2-D) feature map generation, and volume-level classifier. Our architecture could address OCT volume classification using two 2-D image machine learning classification algorithms. Specifically, a convolutional neural network (CNN) model is trained and used as a B-scan feature extractor to construct a 2-D feature map of an OCT volume and volume-level classifiers such as support vector machine and CNN with/without attention mechanism for 2-D feature maps are described. Results: Our proposed methods are validated on the publicly available Duke dataset, which consists of 269 intermediate age-related macular degeneration (AMD) volumes and 115 normal volumes. Fivefold cross-validation was done, and average accuracy, sensitivity, and specificity of 98.17%, 99.26%, and 95.65%, respectively, are achieved. The experiments show that our methods outperform the state-of-the-art methods. Our methods are also validated on our private clinical OCT volume dataset, consisting of 448 AMD volumes and 462 diabetic macular edema volumes. Conclusions: We present a general framework of OCT volume classification based on its 2-D feature map and CNN with attention mechanism and describe its implementation schemes. Our proposed methods could classify OCT volumes automatically and effectively with high accuracy, and they are a potential practical tool for screening of ophthalmic diseases from OCT volume.
Published: 2020

42. Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks

Author: Chunliu He, Yifan Yin, Zhiyong Li, and Jiaqiu Wang
Subjects: Paper, Computer science, Biomedical Engineering, Image processing, Plaque, Amyloid, 01 natural sciences, Padding, Convolutional neural network, 010309 optics, Biomaterials, 0103 physical sciences, Medical imaging, Humans, Segmentation, General, plaque calcification, Contextual image classification, business.industry, Deep learning, Calcinosis, deep learning, Pattern recognition, Image segmentation, intravascular optical coherence tomography, Atomic and Molecular Physics, and Optics, Plaque, Atherosclerotic, Electronic, Optical and Magnetic Materials, Artificial intelligence, Neural Networks, Computer, atherosclerosis, business, Tomography, Optical Coherence
Abstract: Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores=94% for non-zeros padding and F1-score=96% for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability.
Published: 2020

43. Using anatomically defined regions-of-interest to adjust for head-size and probe alignment in functional near-infrared spectroscopy

Author: Xuetong Zhai, Theodore J. Huppert, and Hendrik Santosa
Subjects: Paper, Computer science, Brain activity and meditation, Neuroscience (miscellaneous), Image registration, 01 natural sciences, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, statistical analysis, Position (vector), 0103 physical sciences, functional near-infrared spectroscopy, Radiology, Nuclear Medicine and imaging, Sensitivity (control systems), Radiological and Ultrasound Technology, business.industry, functional brain imaging, Detector, Contrast (statistics), Pattern recognition, Research Papers, Weighting, Functional near-infrared spectroscopy, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract: Significance: Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim: We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task. Approach: We use the optical forward model to project the underlying brain-space ROI into a tapered contrast vector, which defines the relative weighting of the fNIRS channels contributing to the ROI and allows us to test the null hypothesis of no brain activity in this region during a functional task. We demonstrate this method through simulation and compare the sensitivity-specificity of this approach to other conventional methods. Results: We examine the performance of this method in the scenario where head size and probe registration are both an accurately known parameters and where this is subject to unknown experimental errors. This method is compared with the performance of the conventional method using 364 different simulation parameter combinations. Conclusion: The proposed method is always recommended in ROI-based analysis, since it significantly improves the analysis performance without a localizer task, wherever the fNIRS probe registration is known or unknown.
Published: 2020

44. Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions

Author: Ben T. Cox, Ciaran A. Bench, and Andreas Hauptmann
Subjects: Paper, Computer science, Biomedical Engineering, Absolute difference, 01 natural sciences, Convolutional neural network, Standard deviation, Data modeling, 010309 optics, Biomaterials, 03 medical and health sciences, Imaging, Three-Dimensional, 0103 physical sciences, Oximetry, General, quantitative photoacoustics, 030304 developmental biology, 0303 health sciences, business.industry, Deep learning, deep learning, sO2, Pattern recognition, Image segmentation, Real image, 3D modeling, oxygen saturation, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Oxygen, machine learning, Neural Networks, Computer, Artificial intelligence, photoacoustics, Artifacts, business
Abstract: Significance: Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO2 from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models. Results: The mean of the absolute difference between the true mean vessel sO2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. Conclusions: 3-D fully convolutional networks were shown capable of producing accurate sO2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo.
Published: 2020

45. Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics

Author: Martin Wolf, Dominik Wyser, Roger Gassert, Michelle Mattille, Olivier Lambercy, Felix Scholkmann, University of Zurich, and Wyser, Dominik
Subjects: Paper, Computer science, Brain activity and meditation, Neuroscience, FNIRS, brain-computer interface, Short-channel regression, Neuroimaging, Separation (statistics), Neuroscience (miscellaneous), Hemodynamics, 610 Medicine & health, general linear model, 01 natural sciences, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, medicine, functional near-infrared spectroscopy, 2741 Radiology, Nuclear Medicine and Imaging, Radiology, Nuclear Medicine and imaging, 3614 Radiological and Ultrasound Technology, Brain–computer interface, General linear model, Radiological and Ultrasound Technology, business.industry, brain–computer interface, Pattern recognition, 10027 Clinic for Neonatology, Research Papers, Regression, physiological systemic artifacts, medicine.anatomical_structure, 2801 Neuroscience (miscellaneous), Scalp, scalp hemodynamics, Functional near-infrared spectroscopy, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract: Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic “noise” by subtracting the signal measured at a short source–detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties. Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions. Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations]. Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included. Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings., Neurophotonics, 7 (03)
Published: 2020

46. On fractality of functional near-infrared spectroscopy signals: analysis and applications

Author: Laleh Najafizadeh, Sasan Haghani, and Li Zhu
Subjects: Paper, fractal dimension, optical brain imaging, Computer science, Neuroscience (miscellaneous), 01 natural sciences, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, Fractal, Neuroimaging, 0103 physical sciences, medicine, functional near-infrared spectroscopy, Radiology, Nuclear Medicine and imaging, brain computer interfaces, resting state, Brain–computer interface, Radiological and Ultrasound Technology, Resting state fMRI, medicine.diagnostic_test, business.industry, Pattern recognition, visibility graph, Fractal analysis, Research Papers, classification, Functional near-infrared spectroscopy, Graph (abstract data type), Artificial intelligence, business, Functional magnetic resonance imaging, 030217 neurology & neurosurgery
Abstract: Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.
Published: 2020

47. Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies

Author: Xuetong Zhai, Patrick J. Sparto, Hendrik Santosa, Theodore J. Huppert, and Frank A. Fishburn
Subjects: Paper, Computer science, Neuroscience (miscellaneous), 01 natural sciences, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, sensitivity–specificity, Component analysis, 0103 physical sciences, Linear regression, functional near-infrared spectroscopy, Radiology, Nuclear Medicine and imaging, systemic physiological noise, General linear model, Radiological and Ultrasound Technology, business.industry, Statistical model, Pattern recognition, short-separation measurements, noise removal, Research Papers, Communication noise, Principal component analysis, Functional near-infrared spectroscopy, Artificial intelligence, business, 030217 neurology & neurosurgery, Linear filter
Abstract: Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic “brain” responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.
Published: 2020

48. Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning

Author: Mason T. Chen and Nicholas J. Durr
Subjects: Paper, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Swine, optical property, Computer Vision and Pattern Recognition (cs.CV), Biomedical Engineering, Computer Science - Computer Vision and Pattern Recognition, Special Series on Artificial Intelligence and Machine Learning in Biomedical Optics, spatial frequency-domain imaging, 01 natural sciences, Data modeling, Machine Learning (cs.LG), 010309 optics, Biomaterials, Deep Learning, Robustness (computer science), 0103 physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, Animals, Lung, Ground truth, business.industry, Deep learning, Image and Video Processing (eess.IV), Pattern recognition, Oxygenation, Electrical Engineering and Systems Science - Image and Video Processing, Hand, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, machine learning, Snapshot (computer storage), Artificial intelligence, business, Preclinical imaging, Structured light
Abstract: Significance: Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single-snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy. Aim: We introduce OxyGAN, a data-driven, content-aware method to estimate tissue oxygenation directly from single structured-light images. Approach: OxyGAN is an end-to-end approach that uses supervised generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659- and 851-nm sinusoidal illumination. We benchmark OxyGAN by comparing it with SSOP and a two-step hybrid technique that uses a previously developed deep learning model to predict optical properties followed by a physical model to calculate tissue oxygenation. Results: When tested on human feet, cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96.5%. When applied to sample types not included in the training set, such as human hands and pig colon, OxyGAN achieves a 93% accuracy, demonstrating robustness to various tissue types. On average, OxyGAN outperforms SSOP and a hybrid model in estimating tissue oxygenation by 24.9% and 24.7%, respectively. Finally, we optimize OxyGAN inference so that oxygenation maps are computed ∼10 times faster than previous work, enabling video-rate, 25-Hz imaging. Conclusions: Due to its rapid acquisition and processing speed, OxyGAN has the potential to enable real-time, high-fidelity tissue oxygenation mapping that may be useful for many clinical applications.
Published: 2020
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49. Application of a Novel and Improved VGG-19 Network in the Detection of Workers Wearing Masks

Author: Jian Xiao, Shaozhong Cao, Bilong Li, and Jia Wang
Subjects: Paper, History, business.industry, Computer science, Machine vision, Deep learning, Pattern recognition, Convolutional neural network, Computer Science Applications, Education, Data set, Classifier (linguistics), Softmax function, Artificial intelligence, business, Test data, Network model
Abstract: In order to work and travel safely during the outbreak of COVID-19, a method of security detection based on deep learning is proposed by using machine vision instead of manual monitoring. To detect the illegal behaviors of workers without masks in workplaces and densely populated areas, an improved convolutional neural network VGG-19 algorithm is proposed under the framework of tensorflow, and more than 3000 images are collected for model training and testing. Using VGG-19 network model, three FC layers are optimized into one flat layer and two FC layers with reduced parameters. The softmax classification layer of the original model is replaced by a 2-label softmax classifier. The experimental results show that the precision of the model is 97.62% and the recall is 96.31%. The precision of identifying the workers without masks is 96.82%, the recall is 94.07%, and the data set provided has a high precision. For the future social health and safety to provide favorable test data.
Published: 2020

50. Intraoperative quantitative functional brain mapping using an RGB camera

Author: Raphaël Sablong, Laurent Mahieu-Williame, Jacques Guyotat, Charly Caredda, Laure Alston, Michaël Sdika, Bruno Montcel, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme d'Imagerie Multimodale LyonTech (PILoT), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Service de Neurochirurgie D, Hospices Civils de Lyon (HCL), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Paper, intraoperative imaging, Haemodynamic response, Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV]Life Sciences [q-bio], Neuroscience (miscellaneous), RGB camera, 01 natural sciences, Brain mapping, 010309 optics, 03 medical and health sciences, Functional brain, optical imaging, 0302 clinical medicine, Robustness (computer science), Cortex (anatomy), 0103 physical sciences, medicine, Radiology, Nuclear Medicine and imaging, Radiological and Ultrasound Technology, business.industry, functional brain mapping, RGB camera Paper 19077RR, Pattern recognition, Human brain, Monte-Carlo simulations, Research Papers, medicine.anatomical_structure, RGB color model, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Artificial intelligence, business, hemodynamic response, 030217 neurology & neurosurgery, Electrical brain stimulation
Abstract: International audience; Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. However, it still lacks robustness to be used as a clinical standard. In particular, new biomarkers of brain functionality with improved sensitivity and specificity are needed. We present a method for the computation of hemodynamics-based functional brain maps using an RGB camera and a white light source. We measure the quantitative oxy and deoxyhemoglobin concentration changes in the human brain cortex with the modified Beer-Lambert law and Monte Carlo simulations. A functional model has been implemented to evaluate the functional brain areas following neuronal activation by physiological stimuli. The results show a good correlation between the computed quantitative functional maps and the brain areas localized by electrical brain stimulation (EBS). We demonstrate that an RGB camera combined with a quantitative modeling of brain hemodynamics biomarkers can evaluate in a robust way the functional areas during neurosurgery and serve as a tool of choice to complement EBS. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Published: 2019

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