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1. A case study of medical image software evolution and its impact in the medical imaging community

Author

David Viar-Hernández, Borja Rodriguez-Vila, Mario Gil-Correa, Norberto Malpica, and Angel Torrado-Carvajal

Subjects
Medical imaging computing, Open-source software, Software evolution, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Objective: We present the evolution of medical imaging software and its impact on the medical imaging community through the study of four open-source image analysis software platforms: 3D Slicer, FreeSurfer, FSL, and SPM. Materials and methods: We have studied the impact of these software tools over time, measured by the number of scientific citations. Additionally, we have also studied the source code evolution by measuring the lines of code and the tarball size of the stable releases and the changes in programming languages. Results and discussion: The rising number of related scientific publications confirms the popularity of these software tools in the research community, albeit some differences can be observed in the popularity of the tools. Moreover, we demonstrate that source code has evolved to modernize and optimize, at least partially thanks to the collaboration and code sharing with the user community. Furthermore, this evolution reveals an increased use of higher-level programming languages and meta-languages. Conclusions: The study of four open-source packages has revealed certain patterns in the evolution of medical imaging software and their impact on the medical image community. Further analyses and complementary metrics are suggested.

Published
2024
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2. Development of a Super-Resolution Scheme for Pediatric Magnetic Resonance Brain Imaging Through Convolutional Neural Networks

Author

Juan Manuel Molina-Maza, Adrian Galiana-Bordera, Mar Jimenez, Norberto Malpica, and Angel Torrado-Carvajal

Subjects
deep learning (DL), magnetic resonance imaging (MRI), pediatric imaging, sedation, super-resolution (SR), convolutional neural networks (CNN), Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Pediatric medical imaging represents a real challenge for physicians, as children who are patients often move during the examination, and it causes the appearance of different artifacts in the images. Thus, it is not possible to obtain good quality images for this target population limiting the possibility of evaluation and diagnosis in certain pathological conditions. Specifically, magnetic resonance imaging (MRI) is a technique that requires long acquisition times and, therefore, demands the use of sedation or general anesthesia to avoid the movement of the patient, which is really damaging in this specific population. Because ALARA (as low as reasonably achievable) principles should be considered for all imaging studies, one of the most important reasons for establishing novel MRI imaging protocols is to avoid the harmful effects of anesthesia/sedation. In this context, ground-breaking concepts and novel technologies, such as artificial intelligence, can help to find a solution to these challenges while helping in the search for underlying disease mechanisms. The use of new MRI protocols and new image acquisition and/or pre-processing techniques can aid in the development of neuroimaging studies for children evaluation, and their translation to pediatric populations. In this paper, a novel super-resolution method based on a convolutional neural network (CNN) in two and three dimensions to automatically increase the resolution of pediatric brain MRI acquired in a reduced time scheme is proposed. Low resolution images have been generated from an original high resolution dataset and used as the input of the CNN, while several scaling factors have been assessed separately. Apart from a healthy dataset, we also tested our model with pathological pediatric MRI, and it successfully recovers the original image quality in both visual and quantitative ways, even for available examples of dysplasia lesions. We hope then to establish the basis for developing an innovative free-sedation protocol in pediatric anatomical MRI acquisition.

Published
2022
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3. Co-Designing Digital Technologies for Improving Clinical Care in People with Parkinson’s Disease: What Did We Learn?

Author

Mariana H. G. Monje, Sylvie Grosjean, Martin Srp, Laura Antunes, Raquel Bouça-Machado, Ricardo Cacho, Sergio Domínguez, John Inocentes, Timothy Lynch, Argyri Tsakanika, Dimitrios Fotiadis, George Rigas, Evžen Růžička, Joaquim Ferreira, Angelo Antonini, Norberto Malpica, Tiago Mestre, Álvaro Sánchez-Ferro, and iCARE-PD Consortium

Subjects
digital technologies, codesign, digital care, Parkinson’s disease, Chemical technology, TP1-1185
Abstract

The healthcare model is shifting towards integrated care approaches. This new model requires patients to be more closely involved. The iCARE-PD project aims to address this need by developing a technology-enabled, home-based, and community-centered integrated care paradigm. A central part of this project is the codesign process of the model of care, exemplified by the active participation of patients in the design and iterative evaluation of three sensor-based technological solutions. We proposed a codesign methodology used for testing the usability and acceptability of these digital technologies and present initial results for one of them, MooVeo. Our results show the usefulness of this approach in testing the usability and acceptability as well as the opportunity to incorporate patients’ feedback into the development. This initiative will hopefully help other groups incorporate a similar codesign approach and develop tools that are well adapted to patients’ and care teams’ needs.

Published
2023
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4. Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

Author

Mariana H. G. Monje, Sergio Domínguez, Javier Vera-Olmos, Angelo Antonini, Tiago A. Mestre, Norberto Malpica, and Álvaro Sánchez-Ferro

Subjects
Parkinson's disease, kinematics, webcam, telemedicine, artificial intelligence and bio-inspired algorithms, Neurology. Diseases of the nervous system, RC346-429
Abstract

Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam.Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset.Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.

Published
2021
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5. How to Pseudo-CT: A Comparative Review of Deep Convolutional Neural Network Architectures for CT Synthesis

Author

Javier Vera-Olmos, Angel Torrado-Carvajal, Carmen Prieto-de-la-Lastra, Onofrio A. Catalano, Yves Rozenholc, Filomena Mazzeo, Andrea Soricelli, Marco Salvatore, David Izquierdo-Garcia, and Norberto Malpica

Subjects
computed tomography, deep learning, magnetic resonance imaging, neural network, pseudo-CT, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images from the head of 19 healthy women. The second database contains dual echo Dixon-VIBE MR images and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients. Bone structures in the target anatomy were key in choosing the right deep learning approach. This work provides a deep explanation of the architectures in order to know which DCNN fits better each medical application. According to this study, the 3D U-net architecture would be the best option to generate head pseudo-CTs while the 2D Residual-net provides the most accurate results for the pelvis anatomy.

Published
2022
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6. In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study

Author

Jie Luo, Esra Abaci Turk, Carolina Bibbo, Borjan Gagoski, Drucilla J. Roberts, Mark Vangel, Clare M. Tempany-Afdhal, Carol Barnewolt, Judy Estroff, Arvind Palanisamy, William H. Barth, Chloe Zera, Norberto Malpica, Polina Golland, Elfar Adalsteinsson, Julian N. Robinson, and Patricia Ellen Grant

Subjects
Medicine, Science
Abstract

Abstract Fetal health is critically dependent on placental function, especially placental transport of oxygen from mother to fetus. When fetal growth is compromised, placental insufficiency must be distinguished from modest genetic growth potential. If placental insufficiency is present, the physician must trade off the risk of prolonged fetal exposure to placental insufficiency against the risks of preterm delivery. Current ultrasound methods to evaluate the placenta are indirect and insensitive. We propose to use Blood-Oxygenation-Level-Dependent (BOLD) MRI with maternal hyperoxia to quantitatively assess mismatch in placental function in seven monozygotic twin pairs naturally matched for genetic growth potential. In-utero BOLD MRI time series were acquired at 29 to 34 weeks gestational age. Maps of oxygen Time-To-Plateau (TTP) were obtained in the placentas by voxel-wise fitting of the time series. Fetal brain and liver volumes were measured based on structural MR images. After delivery, birth weights were obtained and placental pathological evaluations were performed. Mean placental TTP negatively correlated with fetal liver and brain volumes at the time of MRI as well as with birth weights. Mean placental TTP positively correlated with placental pathology. This study demonstrates the potential of BOLD MRI with maternal hyperoxia to quantify regional placental function in vivo.

Published
2017
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7. Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts.

Author

Felipe Machado, Norberto Malpica, and Susana Borromeo

Subjects
Medicine, Science
Abstract

Open source hardware for scientific equipment needs to provide source files and enough documentation to allow the study, replication and modification of the design. In addition, parametric modeling is encouraged in order to facilitate customization for other experiments. Parametric design using a solid modeling programming language allows customization and provides a source file for the design. OpenSCAD is the most widely used scripting tool for parametric modeling of open source labware. However, OpenSCAD lacks the ability to export to standard parametric formats; thus, the parametric dimensional information of the model is lost. This is an important deficiency because it is key to share the design in the most accessible formats with no information loss. In this work we analyze OpenSCAD and compare it with FreeCAD Python scripts. We have created a parametric open source hardware design to compare these tools. Our findings show that although Python for FreeCAD is more arduous to learn, its advantages counterbalance the initial difficulties. The main benefits are being able to export to standard parametric models; using Python language with its libraries; and the ability to use and integrate the models in its graphical interface. Thus, making it more appropriate to design open source hardware for scientific equipment.

Published
2019
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8. H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry.

Author

Esteban Pardo, Germán González, Jason M Tucker-Schwartz, Shivang R Dave, and Norberto Malpica

Subjects
Medicine, Science
Abstract

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called "cell astronomy" systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

Published
2019
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9. Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

Author

Pedro Miguel Martinez-Girones, Javier Vera-Olmos, Mario Gil-Correa, Ana Ramos, Lina Garcia-Cañamaque, David Izquierdo-Garcia, Norberto Malpica, and Angel Torrado-Carvajal

Subjects
deep learning, image synthesis, PET/MR, pseudo-CT, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports 0.9367 ± 0.0138 (mean ± SD) and 95% confidence interval (0.9221, 0.9512); the experimental Mean Absolute Error (MAE) reports 73.9149 ± 9.2101 HU and 95% confidence interval (66.3383, 81.4915); the Structural Similarity Index Measure (SSIM) reports 0.9943 ± 0.0009 and 95% confidence interval (0.9935, 0.9951); and the experimental Dice coefficient for bone tissue reports 0.7051 ± 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield Units (m = 0.87; adjusted R2 = 0.91; p < 0.001). The Bland–Altman plot shows that the average of the differences is low (−38.6471 ± 199.6100; 95% CI (−429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.

Published
2021
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10. AUTOMATIC CLASSIFICATION OF STRUCTURAL MRI FOR DIAGNOSIS OF NEURODEGENERATIVE DISEASES

Author

Gloria Diaz, Eduardo Romero, Juan Antonio Hernández-Tamames, Vicente Molina, and Norberto Malpica

Subjects
Neurodegenerative disease, structural MRI, pattern classification, SPM, VBM, DARTEL, Biology (General), QH301-705.5
Abstract

This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples.

Published
2010

11. The interplay between functioning problems and symptoms in first episode of psychosis: An approach from network analysis

Author

Ana Izquierdo, María Cabello, Itziar Leal, Blanca Mellor-Marsá, Miriam Ayora, María-Fe Bravo-Ortiz, Ángela Ibáñez, Karina S. MacDowell, Norberto Malpica, Enrique Baca-García, Natalia E. Fares-Otero, Helena Melero, Pilar López-García, Covadonga M. Díaz-Caneja, Jose Luis Ayuso-Mateos, Manuel Durán-Cutilla, Jessica Merchán-Naranjo, Roberto Mediavilla-Torres, Ainoa Muñoz-Sanjosé, Luis Sanchez-Pastor, Monica Dompablo, Patricia Fernández-Martín, Pablo Puras-Rico, Lucía Albarracin-García, Melero Carrasco, Helena, Rodríguez Jiménez, Roberto, Díaz Marsa, Marina Francisca, Arango López, Celso, García-Albea Martín, Julia Isabel, Leza Cerro, Juan Carlos, Leticia León Quismondo, Ana Izquierdo, María Cabello, Itziar Leal, Blanca Mellor-Marsá, Miriam Ayora, María-Fe Bravo-Ortiz, Ángela Ibáñez, Karina S. MacDowell, Norberto Malpica, Enrique Baca-García, Natalia E. Fares-Otero, Helena Melero, Pilar López-García, Covadonga M. Díaz-Caneja, Jose Luis Ayuso-Mateos, Manuel Durán-Cutilla, Jessica Merchán-Naranjo, Roberto Mediavilla-Torres, Ainoa Muñoz-Sanjosé, Luis Sanchez-Pastor, Monica Dompablo, Patricia Fernández-Martín, Pablo Puras-Rico, Lucía Albarracin-García, Melero Carrasco, Helena, Rodríguez Jiménez, Roberto, Díaz Marsa, Marina Francisca, Arango López, Celso, García-Albea Martín, Julia Isabel, Leza Cerro, Juan Carlos, and Leticia León Quismondo

Abstract

The relationship between psychotic symptoms and global measures of functioning has been widely studied. No previous study has assessed so far the interplay between specific clinical symptoms and particular areas of functioning in first-episode psychosis (FEP) using network analysis methods. A total of 191 patients with FEP (age 24.45 ± 6.28 years, 64.9% male) participating in an observational and longitudinal study (AGES-CM) comprised the study sample. Functioning problems were assessed with the WHO Disability Assessment Schedule (WHODAS), whereas the Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity. Network analysis were conducted with the aim of analysing the patterns of relationships between the different dimensions of functioning and PANSS symptoms and factors at baseline. According to our results, the most important nodes were “conceptual disorganization”, “emotional withdrawal”, “lack of spontaneity and flow of conversation”, “delusions”, “unusual thought content”, “dealing with strangers” and “poor rapport”. Our findings suggest that these symptoms and functioning dimensions should be prioritized in the clinical assessment and management of patients with FEP. These areas may also become targets of future early intervention strategies, so as to improve quality of life in this population., Depto. de Psicobiología y Metodología en Ciencias del Comportamiento, Fac. de Psicología, TRUE, pub

Published
2024

18. Co-Designing Digital Technologies for Improving Clinical Care in People with Parkinson’s Disease: What Did We Learn?

Author

Consortium, Mariana Monje, Sylvie Grosjean, Martin Srp, Laura Antunes, Raquel Bouça-Machado, Ricardo Cacho, Sergio Domínguez, John Inocentes, Timothy Lynch, Argyri Tsakanika, Dimitrios Fotiadis, George Rigas, Evžen Růžička, Joaquim Ferreira, Angelo Antonini, Norberto Malpica, Tiago Mestre, Álvaro Sánchez-Ferro, and iCARE-PD

Subjects
digital technologies, codesign, digital care, Parkinson’s disease
Abstract

The healthcare model is shifting towards integrated care approaches. This new model requires patients to be more closely involved. The iCARE-PD project aims to address this need by developing a technology-enabled, home-based, and community-centered integrated care paradigm. A central part of this project is the codesign process of the model of care, exemplified by the active participation of patients in the design and iterative evaluation of three sensor-based technological solutions. We proposed a codesign methodology used for testing the usability and acceptability of these digital technologies and present initial results for one of them, MooVeo. Our results show the usefulness of this approach in testing the usability and acceptability as well as the opportunity to incorporate patients’ feedback into the development. This initiative will hopefully help other groups incorporate a similar codesign approach and develop tools that are well adapted to patients’ and care teams’ needs.

Published
2023
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19. A Systematic Review of Magnetic Resonance Imaging Studies in Perinatally HIV-Infected Individuals

Author

Helena Melero, Manuela Martín-Bejarano, Norberto Malpica, Ana Martinez-de-Aragón, Beatriz Ruiz-Saez, María Isabel González-Tomé, José Tomás Ramos, and Berta Zamora

Subjects
Pediatrics, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Brain morphometry, MEDLINE, Brain, HIV Infections, Magnetic resonance imaging, General Medicine, Disease, Magnetic Resonance Imaging, Infectious Disease Transmission, Vertical, Young Adult, Diffusion Tensor Imaging, Infectious Diseases, Anti-Retroviral Agents, Neuroimaging, Hiv infected, medicine, Humans, Pharmacology (medical), Young adult, Functional magnetic resonance imaging, business
Abstract

Over the past few years, neuroimaging studies have been performed in young adults with perinatally acquired HIV (PHIV) to study the impact of HIV infection on the central nervous system (CNS), but no recent review have been published. This review aims to identify brain areas where PHIV eems to have greater impact taking into account demographic, behavioral, and clinical characteristics in PHIV infected patients. For this purpose, PubMed and Medline searches were carried out which included studies from 2010 to April 2020. We performed a systematic review and included 26 articles using structural (brain morphometry and diffusion tensor imaging) and functional magnetic resonance imaging methods involving 1182 PHIV-infected participants. Ample evidence has been provided of HIV effects on underlying brain structure. However, information recorded in the studies is commonly incomplete and results sometimes contradictory. In addition to future improvements and dissemination of tools for the developing brain MRI processing and analysis, the inclusion of data related to HIV infection itself (including clinical and immunovirological characteristics as well as detailed information about antiretroviral treatment such as age at ART initiation) may be of vital importance to the better understanding of the impact of the disease on CNS.

Published
2023

20. Neighborhood Vulnerability and Disability in First Episode of Psychosis

Author

Ana Izquierdo, María Cabello, Itziar Leal, Iosune Torio, José L. M. Madrigal, Karina S. MacDowell, Roberto Rodriguez-Jimenez, David Rentero, Ángela Ibáñez, Miriam Ayora, Covadonga M. Díaz-Caneja, Renzo Abregú-Crespo, Blanca Mellor-Marsá, Marina Díaz-Marsá, Norberto Malpica, María-Fé Bravo-Ortiz, Enrique Baca-García, Celso Arango, Jose Luis Ayuso-Mateos, and AGES-CM Group

Subjects
Psychiatry and Mental health
Published
2023

28. Evaluation of Deep Learning–Based Approaches to Segment Bowel Air Pockets and Generate Pelvic Attenuation Maps from CAIPIRINHA-Accelerated Dixon MR Images

Author

Norberto Malpica, Sheung Chee Thomas Ng, Ciprian Catana, Onofrio A. Catalano, Javier Vera-Olmos, Ja Reaungamornrat, Ali Kamen, Hasan Sari, Angel Torrado-Carvajal, Manuel A. Morales, and David Izquierdo-Garcia

Subjects
Scanner, Similarity (geometry), Computer science, business.industry, Deep learning, Attenuation, Pattern recognition, Image processing, Magnetic Resonance Imaging, Convolutional neural network, Pelvis, Deep Learning, Positron-Emission Tomography, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Clinical Investigation, Artificial intelligence, Tomography, X-Ray Computed, business, Correction for attenuation
Abstract

Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, potentially introducing bias in the reconstructed PET images. The aims of this work were to develop deep learning–based methods to automatically segment air pockets and generate pseudo-CT images from CAIPIRINHA-accelerated MR Dixon images. Methods: A convolutional neural network (CNN) was trained to segment air pockets using 3-dimensional CAIPIRINHA-accelerated MR Dixon datasets from 35 subjects and was evaluated against semiautomated segmentations. A separate CNN was trained to synthesize pseudo-CT μ-maps from the Dixon images. Its accuracy was evaluated by comparing the deep learning–, model-, and CT-based μ-maps using data from 30 of the subjects. Finally, the impact of different μ-maps and air pocket segmentation methods on the PET quantification was investigated. Results: Air pockets segmented using the CNN agreed well with semiautomated segmentations, with a mean Dice similarity coefficient of 0.75. The volumetric similarity score between 2 segmentations was 0.85 ± 0.14. The mean absolute relative changes with respect to the CT-based μ-maps were 2.6% and 5.1% in the whole pelvis for the deep learning–based and model-based μ-maps, respectively. The average relative change between PET images reconstructed with deep learning–based and CT-based μ-maps was 2.6%. Conclusion: We developed a deep learning–based method to automatically segment air pockets from CAIPIRINHA-accelerated Dixon images, with accuracy comparable to that of semiautomatic segmentations. The μ-maps synthesized using a deep learning–based method from CAIPIRINHA-accelerated Dixon images were more accurate than those generated with the model-based approach available on integrated PET/MRI scanners.

Published
2021

30. ADAPTING MATERIAL DECOMPOSITION ACQUISITIONS FOR DECT CALIBRATIONS IN PROTON THERAPY PLANNING

Author

David Viar-Hernandez, Borja Rodrigez-Vila, Elisabet Canals de las Casas, Isabel Lorenzo Villanueva, Fernando Cerrón, Norberto Malpica, Alejandro Mazal, Juan Castro Novais, Juan Maria Perez Moreno, Juan Antonio Vera Sanchez, and Angel Torrado-Carvajal

Subjects
Biophysics, General Physics and Astronomy, Radiology, Nuclear Medicine and imaging, General Medicine
Published
2022

32. The interplay between functioning problems and symptoms in first episode of psychosis: An approach from network analysis

Author

Angela Ibáñez, Leticia León-Quismondo, Manuel Durán-Cutilla, Lucía Albarracin-García, Itziar Leal, Dolores Saiz-González, Marina Díaz-Marsá, Juan C. Leza, Enrique Baca-García, Julia García-Albea, Ana Izquierdo, Maria Cabello, Blanca Mellor-Marsá, Ainoa Muñoz-Sanjose, Roberto Rodriguez-Jimenez, María Fe Bravo-Ortiz, Pilar Lopez-Garcia, Patricia Fernández-Martín, Helena Melero, Jessica Merchán-Naranjo, Roberto Mediavilla-Torres, Norberto Malpica, Karina S. MacDowell, Celso Arango, Natalia E. Fares-Otero, José Luis Ayuso-Mateos, Pablo Puras-Rico, Miriam Ayora, Covadonga M. Díaz-Caneja, Luis Sanchez-Pastor, Mónica Dompablo, and UAM. Departamento de Psiquiatría

Subjects
Adult, Male, Longitudinal study, Psychosis, Adolescent, Medicina, Population, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Quality of life (healthcare), Intervention (counseling), medicine, Humans, Longitudinal Studies, Functioning, education, Biological Psychiatry, First episode, education.field_of_study, Positive and Negative Syndrome Scale, medicine.disease, 030227 psychiatry, Psychiatry and Mental health, Psychotic Disorders, First episode of psychosis, Quality of Life, Female, Network analysis, Observational study, Psychology, 030217 neurology & neurosurgery, Clinical psychology
Abstract

The relationship between psychotic symptoms and global measures of functioning has been widely studied. No previous study has assessed so far the interplay between specific clinical symptoms and particular areas of functioning in first-episode psychosis (FEP) using network analysis methods. A total of 191 patients with FEP (age 24.45 ± 6.28 years, 64.9% male) participating in an observational and longitudinal study (AGES-CM) comprised the study sample. Functioning problems were assessed with the WHO Disability Assessment Schedule (WHODAS), whereas the Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity. Network analysis were conducted with the aim of analysing the patterns of relationships between the different dimensions of functioning and PANSS symptoms and factors at baseline. According to our results, the most important nodes were “conceptual disorganization”, “emotional withdrawal”, “lack of spontaneity and flow of conversation”, “delusions”, “unusual thought content”, “dealing with strangers” and “poor rapport”. Our findings suggest that these symptoms and functioning dimensions should be prioritized in the clinical assessment and management of patients with FEP. These areas may also become targets of future early intervention strategies, so as to improve quality of life in this population, This work was supported by the Madrid Regional Government (R&D activities in Biomedicine (grant number S2017/BMD-3740 - AGES-CM 2-CM)) and Structural Funds of the European Union. Ana Izquierdo’s work is supported by the PFIS predoctoral program (FI17/00138) from the Instituto de Salud Carlos III (Spain) and co-funded by the European Union (ERDF/ESF, "A way to make Europe”/ “Investing in your future”) and The Biomedical Research Foundation of La Princesa University Hospital. Angela Ib´a˜nez thanks the support of CIBERSAM and of the Spanish Ministry of Science, Innovation and Universities. Instituto de Salud Carlos III (PI16/00834 and PI19/01295) co-financed by ERDF Funds from the European Commission. Covadonga M. Díaz-Caneja holds a Juan Rod´es Grant from Instituto de Salud Carlos III (JR19/00024). Celso Arango was supported by the Spanish Ministry of Science and Innovation. Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/ 024), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds. European Union Seventh Framework Program under grant agreements FP7-4-HEALTH-2009-2.2.1-2-241909 (Project EU-GEI), FP7- HEALTH- 2013-2.2.1-2-603196 (Project PSYSCAN) and FP7- HEALTH-2013- 2.2.1-2-602478 (Project METSY); and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundaci´on Familia Alonso, Fundaci´on Alicia Koplowitz and Fundaci´on Mutua Madrile˜na

Published
2021

35. The relationship between negative symptoms, social cognition, and social functioning in patients with first episode psychosis

Author

María García-López, Miguel Alonso-Sánchez, Itziar Leal, David Martín-Hernández, Javier R. Caso, Covadonga M. Díaz-Caneja, Álvaro Andreu-Bernabeu, Celso Arango, Roberto Rodriguez-Jimenez, Luis Sánchez-Pastor, Marina Díaz-Marsá, Blanca Mellor-Marsá, Ángela Ibáñez, Norberto Malpica, Maria-Fe Bravo-Ortiz, Enrique Baca-Garcia, Jose Luis Ayuso-Mateos, Ana Izquierdo, Juan C. Leza, Miriam Ayora, Sara Boi, Aggie Nuñez-Doyle, Olga Jiménez-Rodríguez, Julia García Albea, Jose Rodríguez Quijano, Jerónimo Saiz-Ruiz, Leticia León-Quismondo, Ainoa Muñoz-Sanjose, Paz Vidal-Villegas, Lucia Albarracin García, Maria Luisa Barrigon-Esteve, and Iosune Torio

Subjects
Social Cognition, Psychiatry and Mental health, Psychotic Disorders, Schizophrenia, Social Interaction, Humans, Social Adjustment, Biological Psychiatry
Abstract

Social functioning is severely affected in psychotic disorders. Negative symptoms and social cognition seem to play an important role in social functioning, although the preponderance and relationship between these three domains is not clear. In this study, we sought to assess the interrelation between social cognition, social functioning, and the expressiveness and experiential factors of negative symptoms in first-episode psychosis (FEP).216 patients, participants in a multicentre study (AGES-CM), comprised our study sample. The WHO Disability Assessment Schedule (WHODAS 2.0) was used to assess functioning, whereas the Positive and Negative Schizophrenia Syndrome Scale (PANSS) was used to measure the severity of negative symptoms, and the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was applied to assess the emotional processing component of social cognition. Network analyses were conducted with the aim of analysing the patterns of relationships between social cognition, social functioning, and the expressiveness and experiential factors of negative symptoms.Our findings suggest that there is a direct relationship between social cognition and social functioning (weight = -.077), but also an indirect connection between them, mediated by the experiential (but not the expressiveness) factor of negative symptoms (weight = 0.300).The importance of the affectation of subdomains of social cognition, as well as the role of negative symptoms, specifically the experiential factor, in the functioning of patients with FEP seems to be relevant. The inclusion of these factors in prevention and treatment programs would thus allow us to reduce their impact on the social functioning of these patients.

Published
2022

36. Hardware Architectures for Real-Time Medical Imaging

Author

Eduardo Alcaín, Pedro R. Fernández, Rubén Nieto, Antonio S. Montemayor, Jaime Vilas, Adrian Galiana-Bordera, Pedro Miguel Martinez-Girones, Carmen Prieto-de-la-Lastra, Borja Rodriguez-Vila, Marina Bonet, Cristina Rodriguez-Sanchez, Imene Yahyaoui, Norberto Malpica, Susana Borromeo, Felipe Machado, and Angel Torrado-Carvajal

Subjects
TK7800-8360, Computer Networks and Communications, Hardware and Architecture, Control and Systems Engineering, biomedical imaging systems, hardware acceleration, Signal Processing, medical imaging, parallel architectures, Electrical and Electronic Engineering, Electronics, medical image analysis
Abstract

Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

Published
2021

38. Seismic Phase Picking Using Convolutional Networks

Author

Carmen Garfias, Norberto Malpica, and Esteban Pardo

Subjects
Seismometer, Computer science, business.industry, 0211 other engineering and technologies, Process (computing), Pattern recognition, Image processing, 02 engineering and technology, Image segmentation, Field (computer science), Data set, Task (computing), General Earth and Planetary Sciences, Artificial intelligence, Electrical and Electronic Engineering, business, 021101 geological & geomatics engineering
Abstract

When a seismometer network records an earthquake, operators will manually review the waveforms and identify the wave phases, a task known as phase picking. Manual phase picking is a time-consuming process that can be automated using machine learning; however, automatic methods have not yet achieved human-level performance, and open-source implementations of state-of-the-art algorithms are not always available. Convolutional networks have revolutionized the field of image processing, where the large amounts of readily available data make possible near-human performance in tasks such as classification and segmentation. Fortunately, phase picking is also an area where thousands of phases are manually picked, which makes convolutional networks a good fit for the processing of this type of data. In this paper, we describe Cospy, an open-source convolutional phase picker that uses a two-stage analysis in which the first stage segments a rough area around the phase, and the second stage regresses the precise location. Our approach was evaluated on the Northern California Earthquake Data Center (NCEDC) data set and, when targeting picks closer than 0.1 s, it achieved an $F_{1}$ -score of 93.13% for P phases and 91.07% for S phases. Our results show that convolutional networks are on track to achieve human-level performance on the task of seismic phase picking and can contribute to decreasing the need for manual analysis. An open-source implementation of the proposed approach, pretrained on the NCEDC data set, can be downloaded at https://github.com/stbnps/cospy .

Published
2019

39. Computer-Vision Techniques for Water-Fat Separation in Ultra High-Field MRI Local Specific Absorption Rate Estimation

Author

Yigitcan Eryaman, Elfar Adalsteinsson, Lawrence L. Wald, Norberto Malpica, Juan Antonio Hernández-Tamames, Angel Torrado-Carvajal, Joaquin L. Herraiz, Esra Abaci Turk, and Radiology & Nuclear Medicine

Subjects
Male, 0206 medical engineering, Separation (statistics), Biomedical Engineering, Magnitude (mathematics), 02 engineering and technology, Volume estimation, Models, Biological, Body Water, Ultra high field, Image Processing, Computer-Assisted, Humans, Computer Simulation, Mathematics, Ground truth, business.industry, Water, Specific absorption rate, Pattern recognition, Image segmentation, Patient specific, Magnetic Resonance Imaging, 020601 biomedical engineering, Adipose Tissue, Female, Artificial intelligence, business, Head, Algorithms
Abstract

Objective: The purpose of this paper is to prove that computer-vision techniques allow synthesizing water-fat separation maps for local specific absorption rate (SAR) estimation, when patient-specific water-fat images are not available. Methods: We obtained ground truth head models by using patient-specific water-fat images. We obtained two different label-fusion water-fat models generating a water-fat multiatlas and applying the STAPLE and local-MAP-STAPLE label-fusion methods. We also obtained patch-based water-fat models applying a local group-wise weighted combination of the multiatlas. Electromagnetic (EM) simulations were performed, and B1+ magnitude and 10 g averaged SAR maps were generated. Results: We found local approaches provide a high DICE overlap (72.6 ± 10.2% fat and 91.6 ± 1.5% water in local-MAP-STAPLE, and 68.8 ± 8.2% fat and 91.1 ± 1.0% water in patch-based), low Hausdorff distances (18.6 ± 7.7 mm fat and 7.4 ± 11.2 mm water in local-MAP-STAPLE, and 16.4 ± 8.5 mm fat and 7.2 ± 11.8 mm water in patch-based) and a low error in volume estimation (15.6 ± 34.4% fat and 5.6 ± 4.1% water in the local-MAP-STAPLE, and 14.0 ± 17.7% fat and 4.7 ± 2.8% water in patch-based). The positions of the peak 10 g-averaged local SAR hotspots were the same for every model. Conclusion: We have created patient-specific head models using three different computer-vision-based water-fat separation approaches and compared the predictions of B1+ field and SAR distributions generated by simulating these models. Our results prove that a computer-vision approach can be used for patient-specific water-fat separation, and utilized for local SAR estimation in high-field MRI. Significance: Computer-vision approaches can be used for patient-specific water-fat separation and for patient specific local SAR estimation, when water-fat images of the patient are not available.

Published
2019

40. DeepEye: Deep convolutional network for pupil detection in real environments

Author

Esteban Pardo, Norberto Malpica, Francisco Javier Vera-Olmos, and Helena Melero

Subjects
business.industry, Computer science, 020101 civil engineering, 02 engineering and technology, Pupil, 0201 civil engineering, Computer Science Applications, Theoretical Computer Science, Computational Theory and Mathematics, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Software
Published
2018

44. Variability in the analysis of a single neuroimaging dataset by many teams

Author

Richard C. Reynolds, Jeremy Hogeveen, Joseph T. McGuire, Vittorio Iacovella, Gregory R. Samanez-Larkin, Cemal Koba, Paolo Avesani, Mikella A. Green, Margaret L. Schlichting, Claudio Toro-Serey, Tristan Glatard, Phui Cheng Lim, Emily A. Yearling, Simon B. Eickhoff, Katherine L. Bottenhorn, Susanne Weis, Andrew Erhart, Doris Pischedda, Mauricio R. Delgado, William A. Cunningham, Robert Langner, Matthew B. Wall, Amr Eed, Jorge Moll, Jeffrey B. Dennison, Wouter D. Weeda, Annabel B. Losecaat Vermeer, Anthony C. Juliano, Felix Hoffstaedter, Julia A. Camilleri, Nadège Bault, Cristian Buc Calderon, Xu Zhang, Gustav Tinghög, Vuong Truong, Leah Bakst, Stephan Heunis, Bertrand Thirion, Colin F. Camerer, Bradley C. Love, Tom Verguts, Luca Cecchetti, Patricia A. Reuter-Lorenz, Camille Maumet, Gabrielle Herman, Giacomo Handjaras, Michalis Kassinopoulos, Anthony Romyn, Xiangzhen Kong, Michael Notter, Nina Lauharatanahirun, Claire Donnat, Norberto Malpica, Jelle J. Goeman, Kamalaker Dadi, Michael Mack, Joke Durnez, Alberto De Luca, Margaret A. Sheridan, Shruti Ray, Colin Hawco, Enrico Glerean, J. Paul Hamilton, Qiang Shen, Sebastian Bobadilla-Suarez, Andrea Leo, David Wisniewski, Ayse Ilkay Isik, Lei Zhang, Bharat B. Biswal, Rotem Botvinik-Nezer, Flora Li, Bronson Harry, Andrew Jahn, Elise Lesage, Jean M. Vettel, Marco Barilari, Adrian I. Onicas, Susan Holmes, Khoi Vo, Aahana Bajracharya, Matthew Hughes, Rui Yuan, Nuri Erkut Kucukboyaci, Remi Gau, Sangsuk Yoon, Hongmi Lee, Alexandru D. Iordan, John Thorp, Russell A. Poldrack, Anna Dreber, Joshua E. Zosky, Dylan M. Nielson, Adriana S. Méndez Leal, Erin W. Dickie, Roeland Hancock, Tiago Bortolini, Kaustubh R. Patil, Monica Y.C. Li, Kristin N. Meyer, Fu Shiguang, Kenny Skagerlund, Juan Carlos de la Torre, Jennifer A. Silvers, R. Alison Adcock, Gustav Nilsonne, Zachary J. Cole, Emilio Sanz-Morales, Charles P. Davis, Michael Joseph, Sangil Lee, Tom Johnstone, Peder M. Isager, Jonathan E. Peelle, Emiliano Ricciardi, Scott A. Huettel, G. Matthew Fricke, Leonardo Tozzi, Brice A. Kuhl, Sagana Vijayarajah, Alexander Bowring, Jeanette A. Mumford, Blazej M. Baczkowski, Shabnam Hakimi, Sebastian Kupek, Michael Kirchler, Yanina Prystauka, Niall W. Duncan, Anna van 't Veer, Emily G. Brudner, Jamil P. Bhanji, Sarah M. Tashjian, Senne Braem, Paolo Papale, Simon R. Steinkamp, Georgios D. Mitsis, Kenneth S. L. Yuen, Magnus Johannesson, David J. White, Alec Smith, Krzysztof J. Gorgolewski, Alexander Leemans, Robert W. Cox, Emanuele Olivetti, Luca Turella, Juergen Huber, Tom Schonberg, David V. Smith, Lysia Demetriou, Peter Sokol-Hessner, Juergen Dukart, Glad Mihai, Taylor Salo, Jean-Baptiste Poline, Angela R. Laird, Sergej A.E. Golowin, Sarah Genon, Adriana Galván, Claus Lamm, Joseph W. Kable, Roni Iwanir, Theo Marins, Matthew R. Johnson, Anais Rodriguez-Thompson, Benjamin Meyer, Stefan Czoschke, Loreen Tisdall, Douglas H. Schultz, Evan N. Lintz, Derek Beaton, Peer Herholz, Olivia Guest, Jenny R. Rieck, Laura Fontanesi, Ekaterina Dobryakova, Alexandre Pérez, Olivier Collignon, Xin Di, Ruud Berkers, Sheryl B. Ball, Carlos González-García, Schuyler W. Liphardt, Edna C. Cieslik, Roland G. Benoit, João F. Guassi Moreira, Helena Melero, Jaime J. Castrellon, Hayley R. Brooks, Chuan-Peng Hu, Felix Holzmeister, Cheryl L. Grady, Thomas E. Nichols, Alba Xifra-Porxas, Steven H. Tompson, Kelsey McDonald, Julia Beitner, Timothy R. Koscik, Tel Aviv University (TAU), Dartmouth College [Hanover], Leopold Franzens Universität Innsbruck - University of Innsbruck, California Institute of Technology (CALTECH), Stockholm School of Economics (SSE), University of Wisconsin-Madison, Duke University [Durham], Fondazione Bruno Kessler [Trento, Italy] (FBK), University of Trento [Trento], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Washington University in Saint Louis (WUSTL), Boston University [Boston] (BU), Virginia Tech [Blacksburg], Université Catholique de Louvain = Catholic University of Louvain (UCL), Plymouth University, Rotman Research Institute at the Baycrest Centre (RRI), University of Amsterdam [Amsterdam] (UvA), Goethe-University Frankfurt am Main, Rutgers University [Newark], Rutgers University System (Rutgers), New Jersey Institute of Technology [Newark] (NJIT), University of Electronic Science and Technology of China [Chengdu] (UESTC), University College of London [London] (UCL), D'Or Institute for Research and Education [Rio de Janeiro], Florida International University [Miami] (FIU), University of Oxford, Vrije Universiteit Brussel (VUB), Universiteit Gent = Ghent University (UGENT), University of Denver, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Jülich Research Centre, School for Advanced Studies Lucca (IMT), University of Nebraska–Lincoln, University of Nebraska System, National Institutes of Health [Bethesda] (NIH), University of Toronto, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Lancashire Teaching Hospital, University of Connecticut (UCONN), University Medical Center [Utrecht], Imperial College London, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE), Kessler Foundation [East Hanover], Stanford University, Taipei Medical University, TMU-ShuangHo Hospital [New Taipei City], Instituto de Neurociencias de Alicante, University of Basel (Unibas), The University of New Mexico [Albuquerque], Zhejiang University of Technology, University of California [Los Angeles] (UCLA), University of California (UC), Université catholique de Lille (UCL), Concordia University [Montreal], Aalto University, Leiden University Medical Center (LUMC), Universiteit Leiden, Linköping University (LIU), Western Sydney University, Centre for Addiction and Mental Health [Toronto] (CAMH), McGill University = Université McGill [Montréal, Canada], Eindhoven University of Technology [Eindhoven] (TU/e), Epilepsy Centre Kempenhaeghe [Heeze], Leibniz-Institut für Resilienzforschung [Mainz] (LIR), Swinburne University of Technology (Hawthorn campus), University of Michigan [Ann Arbor], University of Michigan System, Max Planck Institute for Empirical Aesthetics [Frankfurt am Main], University of Pennsylvania, Max Planck Institute for Psycholinguistics, Carver College of Medicine, University of Iowa, Rutgers New Jersey Medical School (NJMS), University of Oregon [Eugene], University of Vienna [Vienna], Johns Hopkins University (JHU), Nanjing University (NJU), Haskins Laboratories, The Alan Turing Institute, Universidad Rey Juan Carlos [Madrid] (URJC), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Karolinska Institutet [Stockholm], Stockholm University, Université de Lausanne = University of Lausanne (UNIL), Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences (KNAW), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Humboldt University Of Berlin, Technical University of Berlin / Technische Universität Berlin (TU), University of California [Berkeley] (UC Berkeley), Columbia University [New York], Human Research and Engineering Directorate [Aberdeen Proving Ground], Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, University of California [Santa Barbara] (UC Santa Barbara), University of Dayton, Maumet, Camille, Technische Universität Berlin (TU), Leiden University, European Commission, Fundación 'la Caixa', Tel Aviv University [Tel Aviv], University of Innsbruck, University of Electronic Science and Technology of China (UESTC), University of Oxford [Oxford], Universiteit Gent = Ghent University [Belgium] (UGENT), University of Nebraska [Lincoln], Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, University of California, University of Pennsylvania [Philadelphia], Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Lausanne (UNIL), Humboldt-Universität zu Berlin, University of California [Berkeley], University of California [Santa Barbara] (UCSB), Signal Processing Systems, and Human Technology Interaction

Subjects
Data Analysis, Male, Scientific community, Computer science, Decision, Datasets as Topic, Variation (game tree), Field (computer science), 0302 clinical medicine, Multidisciplinary approach, Data Science/methods, Models, Meta-Analysis as Topic, 0303 health sciences, Multidisciplinary, medicine.diagnostic_test, Brain, Magnetic Resonance Imaging, Research Personnel, Datasets as Topic/statistics & numerical data, Neurological, Female, ddc:500, Research Personnel/organization & administration, Models, Neurological, BF, Article, 03 medical and health sciences, Neuroimaging, Functional neuroimaging, Human behaviour, medicine, Humans, 030304 developmental biology, Statistical hypothesis testing, Flexibility (engineering), [SCCO.NEUR]Cognitive science/Neuroscience, Functional Neuroimaging, [SCCO.NEUR] Cognitive science/Neuroscience, Data Science, Reproducibility of Results, Brain/diagnostic imaging, Pipeline (software), Data science, Workflow, Logistic Models, Functional magnetic resonance imaging, Decision making, 030217 neurology & neurosurgery, Software
Abstract

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).

Published
2020

46. Early perfusion changes in multiple sclerosis patients as assessed by MRI using arterial spin labeling

Author

Ignacio Casanova Peña., Pablo García-Polo García, Mar Jimenez De La Peña, Margarita Rubio, Vicente Martínez de Vega, Norberto Malpica, Rafael Arroyo González, and Miguel López Gavilán

Subjects
multiple sclerosis perfusion, bolus arrival time, gray matter-multiple sclerosis, Diagnóstico por imagen, cognitive disability, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Cognitive disabilities, 0302 clinical medicine, Medicina preventiva, medicine, Tecnología médica, Bolus arrival time, medicine.diagnostic_test, business.industry, Multiple sclerosis, Magnetic resonance imaging, Perfusion magnetic resonance imaging, General Medicine, medicine.disease, arterial spin labeling, Arterial spin labeling, Original Article, sense organs, Nuclear medicine, business, Perfusion, 030217 neurology & neurosurgery
Abstract

Background Gadolinium-perfusion magnetic resonance (MR) identifies gray matter abnormalities in early multiple sclerosis (MS), even in the absence of structural differences. These perfusion changes could be related to the cognitive disability of these patients, especially in the working memory. Arterial spin labeling (ASL) is a relatively recent perfusion technique that does not require intravenous contrast, making the technique especially attractive for clinical research. Purpose To verify the perfusion alterations in early MS, even in the absence of cerebral volume changes. To introduce the ASL sequence as a suitable non-invasive method in the monitoring of these patients. Material and Methods Nineteen healthy controls and 28 patients were included. The neuropsychological test EDSS and SDMT were evaluated. Cerebral blood flow and bolus arrival time were collected from the ASL study. Cerebral volume and cortical thickness were obtained from the volumetric T1 sequence. Spearman’s correlation analyzed the correlation between EDSS and SDMT tests and perfusion data. Differences were considered significant at a level of P

Published
2019

47. Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

Author

Javier Vera-Olmos, David Izquierdo-Garcia, Pedro Miguel Martinez-Girones, A. C. Ramos, Norberto Malpica, Mario Gil-Correa, Angel Torrado-Carvajal, and Lina Garcia-Cañamaque

Subjects
pseudo-CT, lcsh:Technology, 030218 nuclear medicine & medical imaging, lcsh:Chemistry, Correlation, 03 medical and health sciences, 0302 clinical medicine, Sørensen–Dice coefficient, Atlas (anatomy), Hounsfield scale, medicine, General Materials Science, Head and neck, lcsh:QH301-705.5, Instrumentation, Mathematics, Fluid Flow and Transfer Processes, medicine.diagnostic_test, lcsh:T, business.industry, Process Chemistry and Technology, General Engineering, deep learning, Magnetic resonance imaging, lcsh:QC1-999, Confidence interval, Computer Science Applications, PET/MR, medicine.anatomical_structure, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, 030220 oncology & carcinogenesis, Tomography, lcsh:Engineering (General). Civil engineering (General), image synthesis, Nuclear medicine, business, lcsh:Physics
Abstract

Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports a 0.9367 ± 0.0138 (mean ± SD) and 95% confidence interval (0.9221, 0.9512), the experimental Mean Absolute Error (MAE) reports 73.9149 ± 9.2101 HU and 95% confidence interval (66.3383, 81.4915), the Structural Similarity Index Measure (SSIM) reports 0.9943 ± 0.0009 and 95% confidence interval (0.9935, 0.9951), and the experimental Dice coefficient for bone tissue reports 0.7051 ± 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield Units (m = 0.87, adjusted R2 = 0.91, p &lt, 0.001). The Bland–Altman plot shows that the average of the differences is low (−38.6471 ± 199.6100, 95% CI (−429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.

Published
2021

48. Real-time patch-based medical image modality propagation by GPU computing

Author

Antonio S. Montemayor, Norberto Malpica, Eduardo Alcain, and Angel Torrado-Carvajal

Subjects
Modality (human–computer interaction), Speedup, Computer science, business.industry, Pipeline (computing), Graphics processing unit, Image registration, Image processing, 030218 nuclear medicine & medical imaging, Computer graphics, 03 medical and health sciences, 0302 clinical medicine, Computer vision, Artificial intelligence, General-purpose computing on graphics processing units, business, 030217 neurology & neurosurgery, Information Systems
Abstract

The synthesis of patient data of a certain medical image modality by applying an image processing pipeline starting from other modality is receiving a lot of interest recently, as it allows to save acquisition time and sometimes avoid radiation to the patient. An example of this is the creation of computerized tomography volumes from magnetic resonance imaging data, which can be useful for several applications such as electromagnetic simulations, cranial morphometry and attenuation correction in PET/MR systems. We present a fast patch-based algorithm for this purpose, implemented using graphics processing unit computing techniques and gaining up to $$\times$$×15.9 of speedup against a multicore CPU solution and up to about $$\times$$×75 against a single core CPU solution.

Published
2016

49. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Author

Franca Wagner, Sorina Camarasu-Pop, Hélène Urien, Isabelle Bloch, Xavier Tomas-Fernandez, Jeremy Beaumont, John Muschelli, Chunliang Wang, Baptiste Laurent, Charles R.G. Guttmann, Tristan Glatard, Michael Kain, Mathieu Simon, Michel Dojat, April Khademi, Martin Styner, Pascal Girard, Thomas Tourdias, Michel M. dos Santos, Wellington Pinheiro dos Santos, Sergi Valverde, Richard McKinley, Abel G. Silva-Filho, Jesse Knight, Eloy Roura, Sandra Vukusic, Francisco Javier Vera-Olmos, François Cotton, Xavier Lladó, Elizabeth M. Sweeney, Senan Doyle, Anne Kerbrat, Olivier Commowick, Mariano Cabezas, Florent Leray, Amirreza Mahbod, Norberto Malpica, Gilles Edan, Roxana Ameli, Christian Barillot, Frederic Cervenansky, Florence Forbes, Simon K. Warfield, Audrey Istace, Jean-Christophe Ferré, Vision, Action et Gestion d'informations en Santé ( VisAGeS ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE ( IRISA_D5 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National des Sciences Appliquées - Rennes ( INSA Rennes ) -Université de Bretagne Sud ( UBS ) -École normale supérieure - Rennes ( ENS Rennes ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Centre Hospitalier Lyon Sud [CHU - HCL] ( CHLS ), Hospices Civils de Lyon ( HCL ), Laboratoire de Traitement de l'Information Medicale ( LaTIM ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest ( CHRU Brest ) -Université de Brest ( UBO ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Mines-Télécom [Paris], Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), 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 ), Service de neurochirurgie [Rennes], Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Service de neurologie [Rennes], Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ), Neuroinflammation: imagerie et thérapie de la sclérose en plaques, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Concordia University [Montreal], Pixyl Medical [Grenoble], Modelling and Inference of Complex and Structured Stochastic Systems ( MISTIS ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire Jean Kuntzmann ( LJK ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), University of Guelph, Department of Electrical and Computer Engineering [TORONTO] ( ECE ), University of Toronto, Royal Institute of Technology [Stockholm] ( KTH ), University of Bern, Johns Hopkins Bloomberg School of Public Health [Baltimore, MD, USA], Visio per computador I robotica ( VICOROB ), Universitat de Girona ( UdG ), Universidade Federal de Pernambuco [Recife] ( UFPE ), Computational Radiology Laboratory [Boston] ( CRL ), Brigham and Women's Hospital [Boston]-Children's Hospital, Laboratoire Traitement et Communication de l'Information ( LTCI ), Télécom ParisTech-Institut Mines-Télécom [Paris]-Université Paris-Saclay, Universidad Rey Juan Carlos [Madrid] ( URJC ), Brigham and Women's Hospital [Boston], Service de Neurologie, CHU Pontchaillou, Rennes, Grenoble Institut des Neurosciences ( GIN ), Université Joseph Fourier - Grenoble 1 ( UJF ) -CHU Grenoble-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Computer Science ( North Carolina State University ), North Carolina State University [Raleigh] ( NCSU ), Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), 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)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Département de Radiologie [CHU de Rennes], Université de Rennes (UR), CHU Pontchaillou [Rennes], CHU Bordeaux [Bordeaux], Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Ryerson University [Toronto], Royal Institute of Technology [Stockholm] (KTH ), Johns Hopkins Bloomberg School of Public Health [Baltimore], Johns Hopkins University (JHU), Research institute of Computer Vision and Robotics [Girona] (VICOROB), Universitat de Girona (UdG), Universidade Federal de Pernambuco [Recife] (UFPE), Computational Radiology Laboratory [Boston] (CRL), Brigham and Women's Hospital [Boston]-Boston Children's Hospital, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Universidad Rey Juan Carlos [Madrid] (URJC), Service de Neurologie [Lyon], CHU Lyon, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French 'Investissements d’Avenir' program) for funding and sponsoring the challenge. This work has also been partly supported by a grant (OFSEP) provided by the French State and handled by the 'Agence nationale de la recherche', within the framework of the 'Investissements d’Avenir' program, under the reference ANR-10-COHO-002. We also thank the French national cohort OFSEP (a French 'Investissements d’Avenir' program), and particularly the imaging group inside this cohort consortium for their constant support, fruitful discussions on the challenge and providing the MR images., ANR-11-INBS-0006,FLI,France Life Imaging(2011), ANR-10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), 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 de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Boston Children's Hospital-Brigham and Women's Hospital [Boston], Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-11-INBS-0006/11-INBS-0006,FLI,France In vivo Imaging(2011), ANR-10-COHO-002-01/10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Images et Modèles, 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), 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 de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-CHU Pontchaillou [Rennes], Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Service Informatique et développements, Department of Electrical and Computer Engineering [University of Toronto] (ECE), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT), Service de Neurologie [CHU Rennes], Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM), Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), RMN et optique : De la mesure au biomarqueur, Edan, Gilles, Infrastructures - France Life Imaging - - FLI2011 - ANR-11-INBS-0006 - INBS - VALID, Cohortes - Observatoire Français de la Sclérose en Plaques - - OFSEP2010 - ANR-10-COHO-0002 - COHO - VALID, 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
Male, Computer science, Data management, MESH: Parenchymal Tissue, lcsh:Medicine, Esclerosi múltiple, computer.software_genre, 030218 nuclear medicine & medical imaging, MESH: Magnetic Resonance Imaging, Machine Learning, 0302 clinical medicine, open science, Image Processing, Computer-Assisted, Segmentation, [ SDV.IB ] Life Sciences [q-bio]/Bioengineering, Image processing -- Digital techniques, lcsh:Science, 610 Medicine & health, Multiple sclerosis lesion, image segmentation, MESH: Machine Learning, Magnetic Resonance Imaging, MESH: Image Processing, Computer-Assisted, Random forest, performance evaluation, MESH: Neural Networks, Computer, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Objective evaluation, Algorithms, MESH: Algorithms, Machine learning, Article, Multiple sclerosis, 03 medical and health sciences, Imatges -- Processament -- Tècniques digitals, distributed computing, Image Interpretation, Computer-Assisted, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Parenchymal Tissue, Retrospective Studies, MESH: Humans, business.industry, Deep learning, Imatge -- Segmentació, lcsh:R, MESH: Retrospective Studies, MESH: Multiple Sclerosis, MESH: Male, Imaging segmentation, computing infrastructure, [ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], lcsh:Q, Neural Networks, Computer, Artificial intelligence, business, computer, MESH: Female, MESH: Image Interpretation, Computer-Assisted, 030217 neurology & neurosurgery
Abstract

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French “Investissements d’Avenir” program) for funding and sponsoring the challenge. This work has also been partly supported by a grant (OFSEP) provided by the French State and handled by the “Agence nationale de la recherche”, within the framework of the “Investissements d’Avenir” program, under the reference ANR-10-COHO-002. We also thank the French national cohort OFSEP (a French “Investissements d’Avenir” program), and particularly the imaging group inside this cohort consortium for their constant support, fruitful discussions on the challenge and providing the MR images

Published
2018

50. Semantic segmentation of mFISH images using convolutional networks

Author

Esteban Pardo, José Mário T. Morgado, and Norberto Malpica

Subjects
FOS: Computer and information sciences, 0301 basic medicine, Manual interpretation, Histology, Databases, Factual, Computer science, Computer Vision and Pattern Recognition (cs.CV), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Cross-validation, Pathology and Forensic Medicine, Machine Learning, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, Image Processing, Computer-Assisted, Segmentation, Spatial analysis, In Situ Hybridization, Fluorescence, Pixel, business.industry, Pattern recognition, Cell Biology, Semantics, Improved performance, 030104 developmental biology, Computer-aided diagnosis, 020201 artificial intelligence & image processing, Artificial intelligence, Neural Networks, Computer, business, Classifier (UML)
Abstract

Multicolor in situ hybridization (mFISH) is a karyotyping technique used to detect major chromosomal alterations using fluorescent probes and imaging techniques. Manual interpretation of mFISH images is a time consuming step that can be automated using machine learning; in previous works, pixel or patch wise classification was employed, overlooking spatial information which can help identify chromosomes. In this work, we propose a fully convolutional semantic segmentation network for the interpretation of mFISH images, which uses both spatial and spectral information to classify each pixel in an end-to-end fashion. The semantic segmentation network developed was tested on samples extracted from a public dataset using cross validation. Despite having no labeling information of the image it was tested on, our algorithm yielded an average correct classification ratio (CCR) of 87.41%. Previously, this level of accuracy was only achieved with state of the art algorithms when classifying pixels from the same image in which the classifier has been trained. These results provide evidence that fully convolutional semantic segmentation networks may be employed in the computer aided diagnosis of genetic diseases with improved performance over the current image analysis methods. © 2018 International Society for Advancement of Cytometry.

Published
2018

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