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Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors’ computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.

Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

Objective: Thyroid nodules are a common occurrence in adults. Although the majority of thyroid nodules are benign, a small percentage are cancerous. The combined phosphorylated histone H2AX (γ-H2AX) and p53-binding protein 1 (53BP1) assay was utilized to detect deoxyribonucleic acid (DNA) damage and DNA repair as biomarkers of the cellular stress response. Using the combined γ-H2AX and 53BP1 assay, we evaluated DNA damage, DNA repair capacity, and malignancy risk in peripheral blood mononuclear cells (PBMCs) of euthyroid individuals with nodular goiter. Materials and Methods: Peripheral blood samples were collected from 33 euthyroid patients with newly diagnosed nodular goiter and 30 healthy control participants. A fully automatic image analysis system was used for analyzing DNA damage (γ-H2AX), including DNA double-strand breaks (DSBs), and DNA repair (53BP1). Results: Euthyroid patients with nodular goiter exhibited a higher mean number of γ-H2AX foci per cell and a higher percentage of apoptotic cells compared to the control subjects (p=0.022 and p=0.005, respectively). Conclusion: This study found considerably higher DNA damage in euthyroid patients with nodular goiter than in control individuals. The increase in DNA damage occurs in the early stages of carcinogenesis. These patients were expected to exhibit compromised DNA repair along with enhanced DNA damage, increasing the risk of carcinogenesis. However, euthyroid patients with nodular goiter might be at a high risk of thyroid malignancy due to the high level of DNA damage. A long-term follow-up of these patients would provide better evidence of the relationship between DNA damage and the malignancy risk of thyroid. [ABSTRACT FROM AUTHOR]

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18, 752-771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-regionwise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object. [ABSTRACT FROM AUTHOR]

Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors. [ABSTRACT FROM AUTHOR]

Purpose: Benign computed tomography (CT) changes due to radiation induced lung injury (RILI) are common following stereotactic ablative radiotherapy (SABR) and can be difficult to differentiate from tumor recurrence. The authors measured the ability of CT image texture analysis, compared to more traditional measures of response, to predict eventual cancer recurrence based on CT images acquired within 5 months of treatment. Methods: A total of 24 lesions from 22 patients treated with SABR were selected for this study: 13 with moderate to severe benign RILI, and 11 with recurrence. Three-dimensional (3D) consolidative and ground-glass opacity (GGO) changes were manually delineated on all follow-up CT scans. Two size measures of the consolidation regions (longest axial diameter and 3D volume) and nine appearance features of the GGO were calculated: 2 first-order features [mean density and standard deviation of density (first-order texture)], and 7 second-order texture features [energy, entropy, correlation, inverse difference moment (IDM), inertia, cluster shade, and cluster prominence]. For comparison, the corresponding response evaluation criteria in solid tumors measures were also taken for the consolidation regions. Prediction accuracy was determined using the area under the receiver operating characteristic curve (AUC) and two-fold cross validation (CV). Results: For this analysis, 46 diagnostic CT scans scheduled for approximately 3 and 6 months posttreatment were binned based on their recorded scan dates into 2-5 month and 5-8 month follow-up time ranges. At 2-5 months post-treatment, first-order texture, energy, and entropy provided AUCs of 0.79-0.81 using a linear classifier. On two-fold CV, first-order texture yielded 73% accuracy versus 76%-77% with the second-order features. The size measures of the consolidative region, longest axial diameter and 3D volume, gave two-fold CV accuracies of 60% and 57%, and AUCs of 0.72 and 0.65, respectively. Conclusions: Texture measures of the GGO appearance following SABR demonstrated the ability to predict recurrence in individual patients within 5 months of SABR treatment. Appearance changes were also shown to be more accurately predictive of recurrence, as compared to size measures within the same time period. With further validation, these results could form the substrate for a clinically useful computer-aided diagnosis tool which could provide earlier salvage of patients with recurrence. [ABSTRACT FROM AUTHOR]

Objectives: Prior study has demonstrated the implementation of quantitative ultrasound (QUS) for determining the therapy response in breast tumour patients. Several QUS parameters quantified from the tumour region showed a significant correlation with the patient's clinical and pathological response. In this study, we aim to identify if there exists such a link between QUS parameters and changes in tumour morphology due to combined ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) using the breast xenograft model (MDA-MB-231). Method: Tumours grown in the hind leg of severe combined immuno-deficient mice were treated with permutations of USMB and HT. Ultrasound radiofrequency data were collected using a 25 MHz array transducer, from breast tumour-bearing mice prior and post-24-hour treatment. Result: Our result demonstrated an increase in the QUS parameters the mid-band fit and spectral 0-MHz intercept with an increase in HT duration combined with USMB which was found to be reflective of tissue structural changes and cell death detected using haematoxylin and eosin and terminal deoxynucleotidyl transferase dUTP nick end labelling stain. A significant decrease in QUS spectral parameters was observed at an HT duration of 60 minutes, which is possibly due to loss of nuclei by the majority of cells as confirmed using histology analysis. Morphological alterations within the tumour might have contributed to the decrease in backscatter parameters. Conclusion: The work here uses the QUS technique to assess the efficacy of cancer therapy and demonstrates that the changes in ultrasound backscatters mirrored changes in tissue morphology. [ABSTRACT FROM AUTHOR]

Glioblastoma is a grade IV pernicious neoplasm occurring in the supratentorial region of brain. As its causes are largely unknown, it is essential to understand its dynamics at the molecular level. This necessitates the identification of better diagnostic and prognostic molecular candidates. Blood‐based liquid biopsies are emerging as a novel tool for cancer biomarker discovery, guiding the treatment and improving its early detection based on their tumour origin. There exist previous studies focusing on the identification of tumour‐based biomarkers for glioblastoma. However, these biomarkers inadequately represent the underlying pathological state and incompletely illustrate the tumour because of non‐recursive nature of this approach to monitor the disease. Also, contrary to the tumour biopsies, liquid biopsies are non‐invasive and can be performed at any interval during the disease span to surveil the disease. Therefore, in this study, a unique dataset of blood‐based liquid biopsies obtained primarily from tumour‐educated blood platelets (TEP) is utilised. This RNA‐seq data from ArrayExpress is acquired comprising human cohort with 39 glioblastoma subjects and 43 healthy subjects. Canonical and machine learning approaches are applied for identification of the genomic biomarkers for glioblastoma and their crosstalks. In our study, 97 genes appeared enriched in 7 oncogenic pathways (RAF‐MAPK, P53, PRC2‐EZH2, YAP conserved, MEK‐MAPK, ErbB2 and STK33 signalling pathways) using GSEA, out of which 17 have been identified participating actively in crosstalks. Using PCA, 42 genes are found enriched in 7 pathways (cytoplasmic ribosomal proteins, translation factors, electron transport chain, ribosome, Huntington's disease, primary immunodeficiency pathways, and interferon type I signalling pathway) harbouring tumour when altered, out of which 25 actively participate in crosstalks. All the 14 pathways foster well‐known cancer hallmarks and the identified DEGs can serve as genomic biomarkers, not only for the diagnosis and prognosis of Glioblastoma but also in providing a molecular foothold for oncogenic decision making in order to fathom the disease dynamics. Moreover, SNP analysis for the identified DEGs is performed to investigate their roles in disease dynamics in an elaborated manner. These results suggest that TEPs are capable of providing disease insights just like tumour cells with an advantage of being extracted anytime during the course of disease in order to monitor it. [ABSTRACT FROM AUTHOR]

Simple Summary: The two most common cross-sectional imaging modalities, computed tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in clinical oncology. The emergence of artificial intelligence (AI)-based tools in medical imaging has been motivated by the desire for greater efficiency and efficacy in clinical care. Although a growing number of new AI tools for narrow-specific tasks in imaging is highly encouraging, the effort to tackle the key challenges to implementation by the worldwide imaging community has yet to be appropriately addressed. In this review, we discuss a few challenges in using AI tools and offer some potential solutions with examples from lung CT and MRI of the abdomen, pelvis, and head and neck (HN) region. As we advance, AI tools may significantly enhance clinician workflows and clinical decision-making. Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients. [ABSTRACT FROM AUTHOR]

Before they become invasive, early cancer cells exhibit specific and characteristic changes that are routinely used by a histopathologist for diagnosis. Currently, these early abnormalities are only detectable ex vivo by histopathology or, non-invasively and in vivo, by optical modalities that have not been clinically implemented due to their complexity and their limited penetration in tissues. Optical coherence tomography (OCT) is a noninvasive medical imaging technology with increasing clinical applications in areas such as ophthalmology, cardiology, gastroenterology, etc. In addition to imaging the tissue micro-structure, OCT can also provide additional information, describing the constituents and state of the cellular components of the tissue. Estimates of the nuclear size, sub-cellular morphological variations, dispersion and index of refraction can be extracted from the OCT images and can serve as diagnostically useful biomarkers. Moreover, the development of fully automated algorithms for tissue segmentation and feature extraction and the application of machine learning, can further enhance the clinical potential of OCT. When fully exploited, OCT has the potential to lead to accurate and sensitive, image-derived, biomarkers for disease diagnosis and treatment monitoring of cancer. [ABSTRACT FROM AUTHOR]

A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with genomic information. It encompasses mining tumor radioimages employing deep learning for the automated extraction of relevant features from the segmented volume of interest (VOI). Gene expression values from surgically extracted tumor tissues are often simultaneously analyzed to determine patient specific features. Association between genomic and radiomic features are also explored in some cases to determine the imaging surrogates. Deep learning and transfer learning are typically exploited for efficient knowledge discovery and decision-making. Some studies on survival prediction ensemble learning and interactive learning are also included. The literature mainly focuses on magnetic resonance imaging (MRI) data of the brain for learning and validation and generally involves the NIH TCIA and TCGA repositories as well as the BraTS Challenge databases. [ABSTRACT FROM AUTHOR]

To study if pre-treatment CT texture features in locally advanced squamous cell carcinoma of laryngo-pharynx can predict long-term local control and laryngectomy free survival (LFS). Image texture features of 60 patients treated with chemoradiation (CTRT) within an ethically approved study were studied on contrast-enhanced images using a texture analysis research software (TexRad, UK). A filtration-histogram technique was used where the filtration step extracted and enhanced features of different sizes and intensity variations corresponding to a particular spatial scale filter (SSF): SSF = 0 (without filtration), SSF = 2 mm (fine texture), SSF = 3–5 mm (medium texture) and SSF = 6 mm (coarse texture). Quantification by statistical and histogram technique comprised mean intensity, standard-deviation, entropy, mean positive pixels, skewness and kurtosis. The ability of texture analysis to predict LFS or local control was determined using Kaplan–Meier analysis and multivariate cox model. Median follow-up of patients was 24 months (95% CI:20–28). 39 (65%) patients were locally controlled at last follow-up. 10 (16%) had undergone salvage laryngectomy after CTRT. For both local control & LFS, threshold optimal cut-off values of texture features were analyzed. Medium filtered-texture feature that were associated with poorer laryngectomy free survival were entropy ≥4.54, (p = 0.006), kurtosis ≥4.18; p = 0.019, skewness ≤−0.59, p = 0.001, and standard deviation ≥43.18; p = 0.009). Inferior local control was associated with medium filtered features entropy ≥4.54; p 0.01 and skewness ≤ – 0.12; p = 0.02. Using fine filters, entropy ≥4.29 and kurtosis ≥−0.27 were also associated with inferior local control (p = 0.01 for both parameters). Multivariate analysis showed medium filter entropy as an independent predictor for LFS and local control (p < 0.001 & p = 0.001). Medium texture entropy is a predictor for inferior local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancer and this can complement clinico-radiological factors in predicting prognosticating these tumors. Texture features play an important role as a surrogate imaging biomarker for predicting local control and laryngectomy free survival in locally advanced laryngo-pharyngeal tumors treated with definitive chemoradiation. [ABSTRACT FROM AUTHOR]

The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field. [ABSTRACT FROM AUTHOR]

Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic “expression maps”, we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone. [ABSTRACT FROM AUTHOR]

Cancer is a generic term for a large group of diseases that can affect any part of the body. Lungs cancer is one of the most common and serious type of cancer. Lung cancer is the leading cause of cancer death worldwide [1]. In US alone, the estimated new lung cancer cases for 2017 are 222, 500 out of which 116, 990 are males and 105, 510 females. Estimated deaths are 155, 870 out of which 84, 590 are male and 71, 280 are female [2]. In India, the estimated cases of men and women in 2012 were 54000 & 17000 respectively [1]. Lungs cancer is a malignant lungs tumor characterized by uncontrolled cell growth in tissues of the lung. If it is left untreated, this growth can spread beyond the lungs by process of metastasis into nearby tissues or other parts of the body [3]. There are mainly two types of lungs cancer small-cell lungs carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC). NSCLC also categorized into three parts- Adenocarcinoma, Squamous cell carcinoma and Large cell carcinoma. The most common symptoms are coughing up blood, weight loss, shortness of breath, cough that does not go away, coughing up blood, fatigue, losing weight without trying, loss of appetite, shortness of breath and wheezing and chest pain. The vast majority (85%) of cases of lungs cancer are due to long -term tobacco smoking. About 10-15% of cases occur in people who have never smoked but it happened due to genetic factors, exposure to radon gas, asbestos, second hand smoke or other form of air pollution. Lungs cancer may be seen on chest radiographs and computed tomography but diagnosis confirmed by biopsy which is usually performed by bronchoscope or CT or by PET/CT guidance. F18-FDG PET/CT scanning is best for all types of cancers because of better sensitivity and specificity compared to anatomical imaging such as CT as it provides estimates of tumor glucose metabolism [4]. Standardized uptake value (SUV) is the semi-quantitative parameter which can be estimated from F18-FDG PET studies and routinely used for characterizing of the tumor and assessment of treatment response evaluation in these patients. The cut off value of SUV is 3.5 above which tumor is malignant otherwise benign [5]. However, there are many variables such as amount of activity injected, blood glucose level, time of injection and weight of patient, which can affect the estimation of SUV. Hence, majority of nuclear medicine physician rely more on their visual assessment and use it for reporting the F18-FDG PET/CT Scan. Now, the nuclear medicine community is looking for another reliable quantitative parameters extracted from the image that will be used in diagnosis and/or treatment response evaluation. Image processing algorithms have potential to assist in lesion (e.g. nodule) detection on PET/CT studies and to assess the stability or change in size of lesion on serial PET/CT studies. Comparison and evaluation of image processing techniques against each other require common data sets and standardized methods for evaluation. Investigators developing image processing algorithms need standardized databases with which to work. Therefore, there is a need for F18-FDG PET/CT image database as research resource for medical image processing. PET image texture analysis was proposed to characterize the heterogeneity of tumor F18-FDG uptake [6-9, 37]. F18-FDG uptake is not homogeneous across the tumor because of necrosis, cell proliferation, micro vessel density, and hypoxia [6, 10-12]. It has been shown that tumor heterogeneity can be associated with disease progression, response to therapy, and malignant behavior of the tumor [6, 13]. Texture analysis refers to a variety of mathematical methods that may be applied to describe the relationship between the grey level intensity of pixels or voxels and their position within an image. An advantage of measuring textural parameters is that it is a post processing technique that can be applied to data acquired during standard clinical imaging protocols thereby maximizing the information that can be derived from standard clinical images. A number of textural features can be derived that provide a measure of intralesional heterogeneity e.g. angular second moment, inverse difference moment, entropy, correlation etc. [6, 14, 26]. Texture analysis has been extensively used in CT images and has given promising results as a predictor of survival and in treatment response assessment in NSCLC and other carcinomas [15-18, 27], but it is still new and emerging field in PET/CT. Very few studies has been found in the literature in which texture analysis is used in F18-FDG PET/CT scans to predict patient outcome and treatment response in oncology[19-22, 34-36]. However, only one study has been found in literature on the prediction of treatment response of NSCLC using texture analysis in F18-FDG PET/CT scans [22]. Local Binary Pattern (LBP) is a method of texture analysis it is based on small area. It is based on the texture spectrum model and provides an additional statistical approach to texture analysis. In texture spectrum model a concept of texture unit is proposed. The texture unit is defined for each pixel value by the eight neighboring pixels values in a 3x3 matrix. Each neighboring pixel is compared to the central pixel and a texture unit value is assigned accordingly. Neighboring intensities with threshold lower values compared to the reference pixel are marked with 0, intensity values equal or greater than the reference pixel are marked with 1. The texture unit is read in starting from upper left corner of the newly calculated 3x3 matrix proceeding clockwise. The intent of this study is to construct a database of F18-FDG PET/ CT images of lung masses. [ABSTRACT FROM AUTHOR]

This proposed work implements various classification approaches to classify the H&E-stained Follicular Lymphoma tissue sample. As part of the process, k-means clustering is used to isolate cytological components like the nucleus extracellular and cytoplasmic regions from images. Then feature space vector is constructed by combining global feature extraction techniques like LBP, LDP, GLCM, and other local feature-extraction techniques. To classify FL images into their respective grades, feature space vectors obtained from feature extraction algorithms are input into a multiclass SVM. Moreover, classification accuracies were explicitly tested with different classifiers like CNN and other pre-trained deep learning networks that can directly operate on raw images without any preprocessing to classify the FL images into their respective grades. The efficacy of different classifiers is presented. This preliminary work provides a proof of concept for incorporating automated FL tissue diagnostic systems into future pathology workflows to supplement the pathologists'. [ABSTRACT FROM AUTHOR]

Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging. [ABSTRACT FROM AUTHOR]

To accurately detect and track the thyroid nodules in a video is a crucial step in the thyroid screening for identification of benign and malignant nodules in computer-aided diagnosis (CAD) systems. Most existing methods just perform excellent on static frames selected manually from ultrasound videos. However, manual acquisition is labor-intensive work. To make the thyroid screening process in a more natural way with less labor operations, we develop a well-designed framework suitable for practical applications for thyroid nodule detection in ultrasound videos. Particularly, in order to make full use of the characteristics of thyroid videos, we propose a novel post-processing approach, called Cache-Track, which exploits the contextual relation among video frames to propagate the detection results into adjacent frames to refine the detection results. Additionally, our method can not only detect and count thyroid nodules, but also track and monitor surrounding tissues, which can greatly reduce the labor work and achieve computer-aided diagnosis. Experimental results show that our method performs better in balancing accuracy and speed. [ABSTRACT FROM AUTHOR]

Objective: Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. Methods: We assemble a retrospective dataset involving 1,487 cases of mammograms in which 644 cases have confirmed malignant mass lesions and 843 have benign lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a leave-one-case-out cross-validation method. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated. Results: Comparing with the principle component analyses, nonnegative matrix factorization, and Chi-squared methods, SVM embedded with RPA yielded a significantly higher case-based lesion classification performance with the area under ROC curve of 0.84 ± 0.01 (p<0.02). Conclusion: The study demonstrates that RPA is a promising method to generate optimal feature vectors and improve SVM performance. Significance: This study presents a new method to develop CAD schemes with significantly higher and robust performance. [ABSTRACT FROM AUTHOR]

Prostate Cancer (PCa) is one among the prominent causes of mortality in men, which can only be reduced by early diagnosis. Multi-parametric Magnetic Resonance Imaging (mp-MRI) is increasingly utilized by clinicians for performing diagnostics tasks because it possesses functional and morphological competencies. Although, manual segmentation of PCa on MRI is a tedious, operator-dependent and time consuming task. Therefore, Computer Aided Diagnosis (CAD) of PCa using mp-MRI images is highly desirable by employing computer-assisted segmentation approaches. In this paper, a method is proposed for segmentation of PCa based on level set with Particle Swarm Optimization (PSO) technique to address the limitations of existing techniques as PSO does not require any cost or objective function to be differentiable and it is easy to implement. The energy function is optimized with PSO based technique. The proposed approach is tested over three different mp-MRI modalities i.e., T2 weighted (T2w), Dynamic Contrast Enhanced (DCE) images and Apparent Diffusion Coefficient (ADC) Maps derived from Diffusion Weighted Images (DWI). The accuracy achieved by PSO based methodology is 7.6% greater than without PSO integration i.e., using Gradient descent with added computational overhead of 0.03 s. The experimental outcomes reveal that the proposed methodology shows better results in terms of considered evaluation metrics when compared with the existing techniques on the I2CVB dataset. The impact of the proposed methodology is that it has the ability for precise segmentation even with intensity inhomogeneity, which validates its applications in clinical treatments. Additionally, the proposed technique reduces the manual interference, which in turn minimizes the execution time. [ABSTRACT FROM AUTHOR]

Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%. [ABSTRACT FROM AUTHOR]

Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre‐processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre‐processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern‐based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern‐based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto‐encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K‐fold. [ABSTRACT FROM AUTHOR]

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer‐free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi‐level thresholding based on Rényi's two‐dimensional entropy with a cultural algorithm (2DReCA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey‐level co‐occurrence matrix and block‐wise elliptical local binary pattern) are calculated based on pre‐processed grey‐scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques. [ABSTRACT FROM AUTHOR]

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases. [ABSTRACT FROM AUTHOR]

Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer‐aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker‐based watershed segmentation. After segmentation, a two‐stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS‐DDSM) database and all the mammographic images that contain masses from the mini‐mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied. [ABSTRACT FROM AUTHOR]

With the rapid increase in the cases of deadly skin cancer, the classification on different types of skin cancer has been emerging as one of the most significant issues in the field of medical image. Several approaches have been proposed to help in diagnosing the categories of the skin lesions by means of traditional features or leveraging the widely used deep learning models. However, there are lack of the integrated frameworks to combine the hand‐crafted traditional features and the deep Conv‐features. Furthermore, the effective way to extract global and local features is also conducive to distinguish the specific lesions from normal skin. Hence, in this study, the authors present an integrated model to acquire more representative global–local features including the traditional local binary pattern features and deep Conv‐features. In addition, several fusion strategies have conducted on the Global‐DNN and Local‐DNN for better performance. In order to extract more explicit features from the specific lesion areas, a target segmentation method based on visual saliency detection is employed to eliminate the background interference. Experimental results on ISIC‐2017 skin cancer dataset demonstrate that the proposed Global‐DNN and Global‐Local models can obtain more effective feature representation which achieve outperformed results for skin cancer classification. [ABSTRACT FROM AUTHOR]

Chest X-ray (CXR) images are usually used to identify the causes of patients’ symptoms, including the classes of lung or heart disorders. In visualization examination, CXR imaging in anterior–posterior (A–P) views is a preliminary screening method used by clinicians or radiologists to diagnose possible lung abnormalities, such as pneumothorax (Pt), emphysema (E), infiltration (In), lung cancer (M), pneumonia (P), pulmonary fibrosis (F), and pleural effusion (Ef). However, the identification of the causes of multiple abnormalities associated with coexisting conditions presents a challenge. In ruling out a suspected lung disease, the signs and symptoms of physical conditions need to be identified to arrive at a definitive diagnosis. In addition, low contrast CXR images and manual inspection restrict automated screening applications. Hence, this study aims to propose an iterated function system (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CXR images and to improve screening accuracy. For digital image processes, a two-dimensional (2D) fractional-order convolution is used to enhance symptomatic features. The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. The accuracy rate is thus improved. Pooling is performed to reduce the dimensions of the feature patterns and speed up complex computations. A gray relational analysis-based classifier is used to identify the possible classes of the signs and symptoms of lung diseases. For digital CXR images in A-P view, the proposed multilayer machine learning classifier with k-fold cross-validation presents promising results in screening lung diseases and improving screening accuracy rate relative to traditional methods. The proposed classifier is evaluated in terms of recall (99.6%), precision (87.78%), accuracy (88.88%), and F1 score (0.9334). [ABSTRACT FROM AUTHOR]

Ultrasound (US) imaging is the leading diagnostic method for assessing the early‐stage thyroid nodule. However, the visual evaluation of nodules can be influenced by the subjectivity of radiologists' interpretations. Computer‐aided Diagnostic (CAD) systems can be useful in classifying these nodules according to their benign or malignant nature. The extraction of the characteristics, which relate in the author's case to the US of thyroid nodules, is essential in the differentiation of these nodules. The complex nature of images, however, generates a significant number of features, many of which are either redundant or irrelevant. This study presents a new CAD system that has been developed to categorise thyroid nodules. In this survey, 447 US images of thyroid nodules were retained. These images were used to extract features using statistical features extraction methods. A feature selection method based on the multi objective particle swarm optimisation algorithm was used to choose the most relevant and non‐redundant ones. Then, support vector machine (SVM) and random forests (RFs) were applied to classify these nodules. 10‐fold cross‐validation was used to assess the classification performance metrics. Their proposed CAD has reached a maximum accuracy of 94.28% for SVM; and 96.13% for RF using the contour‐based ROI. [ABSTRACT FROM AUTHOR]

BACKGROUND: In regular examinations, it may be difficult to visually identify benign and malignant liver tumors based on plain computed tomography (CT) images. RCAD (radiomics-based computer-aided diagnosis) has proven to be helpful and provide interpretability in clinical use. OBJECTIVE: This work aims to develop a CT-based radiomics signature and investigate its correlation with malignant/benign liver tumors. METHODS: We retrospectively analyzed 168 patients of hepatocellular carcinoma (malignant) and 117 patients of hepatic hemangioma (benign). Texture features were extracted from plain CT images and used as candidate features. A radiomics signature was developed from the candidate features. We performed logistic regression analysis and used a multiple-regression coefficient (termed as R) to assess the correlation between the developed radiomics signature and malignant/benign liver tumors. Finally, we built a logistic regression model to classify benign and malignant liver tumors. RESULTS: Thirteen features were chosen from 1223 candidate features to constitute the radiomics signature. The logistic regression analysis achieved an R = 0.6745, which was much larger than Rα = 0.3703 (the critical value of R at significant level α = 0.001). The logistic regression model achieved an average AUC of 0.87. CONCLUSIONS: The developed radiomics signature was statistically significantly correlated with malignant/benign liver tumors (p < 0.001). It has potential to help enhance physicians' diagnostic abilities and play an important role in RCADs. [ABSTRACT FROM AUTHOR]

Noninvasive diagnosis of lung cancer in early stages is one task where radiomics helps. Clinical practice shows that the size of a nodule has high predictive power for malignancy. In the literature, convolutional neural networks (CNNs) have become widely used in medical image analysis. We study the ability of a CNN to capture nodule size in computed tomography images after images are resized for CNN input. For our experiments, we used the National Lung Screening Trial data set. Nodules were labeled into 2 categories (small/large) based on the original size of a nodule. After all extracted patches were re-sampled into 100-by-100- pixel images, a CNN was able to successfully classify test nodules into small- and large-size groups with high accuracy. To show the generality of our discovery, we repeated size classification experiments using Common Objects in Context (COCO) data set. From the data set, we selected 3 categories of images, namely, bears, cats, and dogs. For all 3 categories a 5-×2-fold cross-validation was performed to put them into small and large classes. The average area under receiver operating curve is 0.954, 0.952, and 0.979 for the bear, cat, and dog categories, respectively. Thus, camera image rescaling also enables a CNN to discover the size of an object. The source code for experiments with the COCO data set is publicly available in Github (https://github.com/VisionAI-USF/COCO_Size_Decoding/). [ABSTRACT FROM AUTHOR]

aim of this study was to explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of the parotid gland. Materials and Methods: Contrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of the parotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectively discussed by the maximum Youden's index. Results: All the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P< 0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P> 0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean),  0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden's index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%). Conclusion: Energy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of the parotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of the parotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients. [ABSTRACT FROM AUTHOR]

Breast cancer becomes a significant public health problem in the world. During the early detection of breast cancer, it is a very challenging task to classify accurately the benign–malignant patterns in digital mammograms. This study proposes a new fully automated computer‐aided diagnosis (CAD) system for breast cancer diagnosis with high‐accuracy and low‐computational requirements. The expectation–maximisation algorithm is investigated to extract automatically the region of interests (ROIs) within mammograms. The standard shape, statistical, and textural features of ROIs are extracted and combined with multi‐resolution and multi‐orientation features derived from a new feature extraction technique based on wavelet‐based contourlet transform. A hybrid feature selection approach based on combining the support vector machine recursive feature elimination with correlation bias reduction algorithm is proposed. Also, the authors investigate a new similarity‐based learning algorithm, called Q, for benign–malignant classification. The proposed CAD system is applied to real clinical mammograms, and the experimental results demonstrate the superior performance of the proposed CAD system over other existing CAD systems in terms of accuracy 98.16%, sensitivity 98.63%, specificity 97.80%, and computational time 2.2 s. This reveals the effectiveness of the proposed CAD system in improving the accuracy of breast cancer diagnosis in real‐time systems. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to introduce a new species of features to improve the diagnosis efficiency of acute lymphoblastic leukaemia from microscopic images. First, the authors segmented nuclei by the k‐means and watershed algorithms. They extracted three sets of geometrical, statistical, and chaotic features from nuclei images. Six chaotic features were extracted by calculating the fractal dimension from five sub‐images driven from the nuclei images, with their grey levels being modified. The authors classified the images into binary and multiclass types via the support vector machine algorithm. They conducted principal component analysis for dimensional reduction of feature space and then evaluated the proposed algorithm for the overfitting problem. The obtained overall results represent 99% accuracy, 99% specificity, and 97% sensitivity values in the classification of six‐cell groups. The difference between the train and test errors was <3%, which proves that the classification performance had improved by using the multifractal features. [ABSTRACT FROM AUTHOR]

Purpose: Manual delineation of head and neck (H&N) organ‐at‐risk (OAR) structures for radiation therapy planning is time consuming and highly variable. Therefore, we developed a dynamic multiatlas selection‐based approach for fast and reproducible segmentation. Methods: Our approach dynamically selects and weights the appropriate number of atlases for weighted label fusion and generates segmentations and consensus maps indicating voxel‐wise agreement between different atlases. Atlases were selected for a target as those exceeding an alignment weight called dynamic atlas attention index. Alignment weights were computed at the image level and called global weighted voting (GWV) or at the structure level and called structure weighted voting (SWV) by using a normalized metric computed as the sum of squared distances of computed tomography (CT)‐radiodensity and modality‐independent neighborhood descriptors (extracting edge information). Performance comparisons were performed using 77 H&N CT images from an internal Memorial Sloan‐Kettering Cancer Center dataset (N = 45) and an external dataset (N = 32) using Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of HD, median of maximum surface distance, and volume ratio error against expert delineation. Pairwise DSC accuracy comparisons of proposed (GWV, SWV) vs single best atlas (BA) or majority voting (MV) methods were performed using Wilcoxon rank‐sum tests. Results: Both SWV and GWV methods produced significantly better segmentation accuracy than BA (P < 0.001) and MV (P < 0.001) for all OARs within both datasets. SWV generated the most accurate segmentations with DSC of: 0.88 for oral cavity, 0.85 for mandible, 0.84 for cord, 0.76 for brainstem and parotids, 0.71 for larynx, and 0.60 for submandibular glands. SWV's accuracy exceeded GWV's for submandibular glands (DSC = 0.60 vs 0.52, P = 0.019). Conclusions: The contributed SWV and GWV methods generated more accurate automated segmentations than the other two multiatlas‐based segmentation techniques. The consensus maps could be combined with segmentations to visualize voxel‐wise consensus between atlases within OARs during manual review. [ABSTRACT FROM AUTHOR]

Background: Grading of meningiomas is important in the choice of the most effective treatment for each patient.Purpose: To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images.Study Type: Retrospective.Population: In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III.Field Strength/sequence: 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1 W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2 ).Assessment: WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception-V3 and AlexNet DCNNs was tested on ADC maps and PCT1 W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance.Statistical Test: Leave-one-out cross-validation.Results: The application of the Inception-V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88-0.98). Remarkably, only 1/38 WHO Grade II-III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59-0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II-III cases. The discriminating accuracy of both DCNNs on postcontrast T1 W images was low, with Inception-V3 displaying an AUC of 0.68 (95% CI, 0.59-0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45-0.64).Data Conclusion: DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1 W images.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152-1159. [ABSTRACT FROM AUTHOR]

Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach pre-set goals without specific programming. Machine learning tools can help to identify high-risk populations, prescribe personalized screening tests and enrich patient populations that are most likely to benefit from advanced imaging tests. AI algorithms can also help to plan personalized therapies and predict the impact of genomic variations on the sensitivity of normal and tumor tissue to chemotherapy or radiation therapy. The two main bottlenecks for successful AI applications in pediatric oncology imaging to date are the needs for large data sets and appropriate computer and memory power. With appropriate data entry and processing power, deep convolutional neural networks (CNNs) can process large amounts of imaging data, clinical data and medical literature in very short periods of time and thereby accelerate literature reviews, correct diagnoses and personalized treatments. This article provides a focused review of emerging AI applications that are relevant for the pediatric oncology imaging community. [ABSTRACT FROM AUTHOR]

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature. [ABSTRACT FROM AUTHOR]

Advances in the management of genitourinary neoplasms have resulted in a trend towards providing patients with personalized care. Texture analysis of medical images, is one of the tools that is being explored to provide information such as detection and characterization of tumors, determining their aggressiveness including grade and metastatic potential and for prediction of survival rates and risk of recurrence. In this article we review the basic principles of texture analysis and then detail its current role in imaging of individual neoplasms of the genitourinary system. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the leading causes of death in the world. Although early detection of lung tumours (nodules) can remarkably diminish the mortal rate, precise detection of them is not always possible by visual inspection of the computerised tomography images. Since nodules with different sizes have non‐uniform shape and brightness, texture attributes and also the gradient of orientation can be good candidate features, which have been used for this purpose. They determined the co‐occurrence matrix of the extended local binary pattern (ELBP) along with weighted orientation difference (WOD) for each sub‐region of the lung area. Local binary pattern is a texture descriptor that can extract the discriminative features efficiently. The proposed ELBP is rotation invariant and suitable to describe non‐uniform patterns. Moreover, WOD as a structural feature uses the magnitude of each edge as the weight of its orientation difference. After constructing the co‐occurrence matrix, discriminative features were extracted from this matrix and fed into a support vector machine in order to classify each sub‐region as a cancerous (nodule) or normal tissue. The proposed method was compared to some of state‐of‐the‐art nodule detection methods and was assessed over several real datasets in terms of specificity, sensitivity and accuracy. [ABSTRACT FROM AUTHOR]

Pectoral muscle segmentation is a very important step in any Computer Aided Detection (CAD) system, for automatic detection of different possible signs of malignancy in the mammographic image. Presence of high‐density glandular tissues overlapping pectoral muscle boundary, presence of pectoral muscle of small size and low density, presence of pectoralis minor etc., poses a great challenge for researchers working on the problem of automatic pectoral muscle boundary detection. Gabor filter has been used by many researchers to detect textural edges. The ability that it can be tuned along a particular direction, makes it a very important image processing tool. In the proposed research work a Gabor filter tuned in the direction of pectoral muscle boundary is applied on the region of interest (ROI) containing pectoral muscle. Finally, the magnitude and phase response of Gabor filtered ROI image along with an edge‐connect and region merge algorithm is used to detect pectoral muscle boundary. The proposed method is validated using 200 mammograms from the miniMIAS database and 200 mammograms from INbreast database. With an accuracy of 97.50% for miniMIAS database and 94.50% for INbreast database, the proposed method outperforms many state‐of‐the‐art methods which show its suitability for CAD Systems. [ABSTRACT FROM AUTHOR]

Objective: This study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scattering network. Methods: In our approach, the FrScatNet is constructed based on the fractional wavelet transform (FRWT). The fractional scattering coefficients are iteratively computed using FRWTs and modulus operators. The feature vectors constructed by fractional scattering coefficients are usually used for signal classification. In this paper, an application example of the FrScatNet is provided in order to assess its performance on pathological images. First, the FrScatNet extracts feature vectors from patches of the original histological images under different orders. Then we classify those patches into target (benign or malignant) and background groups. And the FrScatNet property is analyzed by comparing error rates computed from different fractional orders, respectively. Based on the above pathological image classification, a gland segmentation algorithm is proposed by combining the boundary information and the gland location. Results: The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is improved in fractional scattering domain. We also compare the FrScatNet-based gland segmentation method with those proposed in the 2015 MICCAI Gland Segmentation Challenge and our method achieves comparable results. Conclusion: The FrScatNet is shown to achieve accurate and robust results. More stable and discriminative fractional scattering coefficients are obtained by the FrScatNet in this paper. Significance: The added fractional order parameter is able to analyze the image in the fractional scattering domain. [ABSTRACT FROM AUTHOR]

Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non‐subsampled shearlet transform (NSST) and simplified pulse‐coupled neural network model (S‐PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y‐component of PET image are decomposed into low‐frequency (LF) and high‐frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S‐PCCN which is motivated by an adaptive‐linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency. [ABSTRACT FROM AUTHOR]

Classification of benign and malignant pulmonary nodules can provide useful indicators for estimating the risk of lung cancer. In this study, an improved random forest (RF) algorithm is proposed for classification of benign and malignant pulmonary nodules in thoracic computed tomography images. First, an improved random walk algorithm is proposed to automatically segment pulmonary nodules. Then, intensity, geometric and texture features based on the grey‐level co‐occurrence matrix, rotation invariant uniform local binary pattern and Gabor filter methods are combined to generate an effective and discriminative feature vector. Mutual information is employed to reduce the dimensionality. Finally, an improved RF classifier is trained to classify benign and malignant nodules. An appropriate feature subset is selected by the bootstrap method and an effective combination method is introduced to predict a class label. The proposed classification method on the lung images dataset consortium dataset achieves a sensitivity of 0.92 and the area under the receiver‐operating‐characteristic curve of 0.95. An additional evaluation is performed on another dataset coming from General Hospital of Guangzhou Military Command. A mean sensitivity and a mean specificity of the proposed method are 0.85 and 0.82, respectively. Experimental results demonstrate that the proposed method achieves the satisfactory classification performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To capture perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then, a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast-based saliency and object-center-based saliency together. The contrast-based saliency map highlights image parts that show different appearances compared with surrounding areas, whereas the object-center-based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with multiple kernel boosting is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multilevel WBU saliency maps. We finally integrate these two-stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs better compared with state-of-the-art saliency methods to detect polyps. [ABSTRACT FROM AUTHOR]

Purpose: Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention. Methods: This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively. results: 12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD) classification accuracy of 93.33 (±3.52)%, sensitivity of 88.33 (± 4.12)%, and specificity of 96.89 (± 3.88)%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC) value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively. conclusion: Our results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade. [ABSTRACT FROM AUTHOR]

Partial nephrectomy (PN) is the recommended procedure over radical nephrectomy (RN) for patients with renal masses less than 4 cm in diameter (Stage T1a). Patients with less than 4 cm renal masses can also be treated with PN, but have a higher risk for positive surgical margins (PSM). PSM, when present, are indicative of poor clinical outcomes. The current gold‐standard histopathology method is not well‐suited for the identification of PSM intraoperatively due to processing time and destructive nature. Here, video‐rate structured illumination microscopy (VR‐SIM) was investigated as a potential tool for PSM detection during PN. A clinical image atlas assembled from ex vivo renal biopsies provided diagnostically useful images of benign and malignant kidney, similar to permanent histopathology. VR‐SIM was then used to image entire parenchymal margins of tumor resection covering up to >1800× more margin surface area than standard histology. Aided by the image atlas, the study pathologist correctly classified all parenchymal margins as negative for PSM with VR‐SIM, compared to standard postoperative pathology. The ability to evaluate large surgical margins in a short time frame with VR‐SIM may allow it to be used intraoperatively as a “safety net” for PSM detection, allowing more patients to undergo PN over RN. [ABSTRACT FROM AUTHOR]

Purpose The sensitivity of the magnetization transfer ratio (MTR) and apparent diffusion coefficient (ADC) for early detection of brain metastases was investigated in mice and humans. Methods Mice underwent MRI twice weekly for up to 31 d following intracardiac injection of the brain-homing breast cancer cell line MDA-MB231-BR. Patients with small cell lung cancer underwent quarterly MRI for 1 year. MTR and ADC were measured in regions of metastasis and matched contralateral tissue at the final time point and in registered regions at earlier time points. Texture analysis and linear discriminant analysis were performed to detect metastasis-containing slices. Results Compared with contralateral tissue, mouse metastases had significantly lower MTR and higher ADC at the final time point. Some lesions were visible at earlier time points on the MTR and ADC maps: 24% of these were not visible on corresponding T2-weighted images. Texture analysis using the MTR maps showed 100% specificity and 98% sensitivity for metastasis at the final time point, with 77% sensitivity 2-4 d earlier and 46% 5-8 d earlier. Only 2 of 16 patients developed metastases, and their penultimate scans were normal. Conclusions Some brain metastases may be detected earlier on MTR than conventional T2; however, the small gain is unlikely to justify 'predictive' MRI. Magn Reson Med 77:1987-1995, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. [ABSTRACT FROM AUTHOR]

Automatic identification of abnormal cervical cells, including feature representation, feature combination and classification strategy, is highly demanded in women's annual cervical cancer screenings. However, previous methods only deal with one or two of these three phases, and currently there is few complete framework for this problem. A novel three‐phrase boosting framework is proposed for the detection of abnormal cells from cervical smear images. First, the authors extract 160 dimensional features with respect to each cervical cell from three aspects, including cytology morphology, chromatin pathology and region intensity. In particular, 106 dimensional chromatin pathology features are newly adopted to describe the nucleus textural transformation. Second, an adaptive feature combination method is introduced to select the optimal feature patterns, which can combine all features using a reinforced margin‐based approach with the heuristic knowledge. Finally, a two‐stage classification strategy is presented to reduce erroneous classification abnormal cells using two different classifiers. Experimental results achieve state‐of‐the‐art performance and the proposed framework outperforms the other 16 compared detection methods. [ABSTRACT FROM AUTHOR]

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. [ABSTRACT FROM AUTHOR]