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1. Ultra-high field 7 T imaging in multiple sclerosis

Author

Valeria Barletta, Constantina A. Treaba, Céline Louapre, Caterina Mainero, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CIC Pitié BT, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), and Harvard Medical School [Boston] (HMS)

Subjects
0301 basic medicine, Pathology, medicine.medical_specialty, Cerebellum, Multiple Sclerosis, [SDV]Life Sciences [q-bio], Thalamus, White matter, Lesion, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Microglia, business.industry, Multiple sclerosis, Meninges, Spinal cord, medicine.disease, Magnetic Resonance Imaging, White Matter, 3. Good health, 030104 developmental biology, medicine.anatomical_structure, Neurology, Spinal Cord, Disease Progression, Neurology (clinical), medicine.symptom, business, 030217 neurology & neurosurgery
Abstract

International audience; Purpose of review: Ultra-high field 7 T MRI has multiple applications for the in vivo characterization of the heterogeneous aspects underlying multiple sclerosis including the identification of cortical lesions, characterization of the different types of white matter plaques, evaluation of structures difficult to assess with conventional MRI (thalamus, cerebellum, spinal cord, meninges).Recent findings: The sensitivity of cortical lesion detection at 7 T is twice than at lower field MRI, especially for subpial lesions, the most common cortical lesion type in multiple sclerosis. Cortical lesion load accrual is independent of that in the white matter and predicts disability progression.Seven Tesla MRI provides details on tissue microstructure that can be used to improve white matter lesion characterization. These include the presence of a central vein, whose identification can be used to improve multiple sclerosis diagnosis, or the appearance of an iron-rich paramagnetic rim on susceptibility-weighted images, which corresponds to iron-rich microglia at the periphery of slow expanding lesions. Improvements in cerebellar and spinal cord tissue delineation and lesion characterization have also been demonstrated.Summary: Imaging at 7 T allows assessing more comprehensively the complementary pathophysiological aspects of multiple sclerosis, opening up novel perspectives for clinical and therapeutics evaluation.

Published
2020

2. Spatial distribution of multiple sclerosis lesions in the cervical spinal cord

Author

Kouhei Kamiya, Massimo Filippi, Marios C. Yiannakas, Ferran Prados, Rohit Bakshi, Hugh Kearney, Sridar Narayanan, Pierre Labauge, Timothy M. Shepherd, Jean-Christophe Brisset, Russell Ouellette, Tobias Granberg, Ren Zhuoquiong, Caterina Mainero, Julien Cohen-Adad, Atef Badji, Jean Pelletier, Josefina Maranzano, Erik Charlson, Leszek Stawiarz, Seth A. Smith, Yasuhiko Tachibana, Yaou Liu, Charley Gros, Masaaki Hori, Dominique Eden, Olga Ciccarelli, Virginie Callot, Daniel S. Reich, Jennifer Lefeuvre, Gilles Edan, Sara M. Dupont, Govind Nair, Henitsoa Rasoanandrianina, Elise Bannier, Paola Valsasina, Jason F. Talbott, Shahamat Tauhid, Jan Hillert, Lydia Chougar, Anne Kerbrat, Constantina A. Treaba, Maria A. Rocca, Benjamin De Leener, Bertrand Audoin, École Polytechnique de Montréal (EPM), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Xuanwu Hospital of Capital, Deutsche Telekom Group, Department of Computer Science [Liverpool], University of Liverpool, Karolinska Institutet [Stockholm], Department of Radiology and Biomedical Imaging [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), 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), Service de Neurologie [Rennes] = Neurology [Rennes], CHU Pontchaillou [Rennes], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), Translational imaging group [London] (TIG), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL)-University College of London [London] (UCL)-Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Vanderbilt University [Nashville], Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), National Institute of Radiological Sciences (NIRS), Juntendo University Hospital [Tokyo], McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Funded by the Canada Research Chair in Quantitative Magnetic Resonance Imaging, CIHR-FDN-143263, FRQS-28826, FRQNT-2015-PR-182754, NSERC-435897–2013, QBIN, NMSS RG-1501–02840 (S.A.S.), NIH/NINDS R21 NS087465–01 (S.A.S.), NIH/NEI R01EY023240 (S.A.S.), DoD W81XWH-13–0073 (S.A.S.), the Intramural Research Program of NIH/NINDS (J.L., D.S.R., G.N.), PHRC (EMISEP), NIH/NINDS R01NS078322–02 (C.M.), ClinicalTrials.gov (NCT02117375), CNRS, 'Fondation A*midex-Investissements d’Avenir', the Stockholm County Council (ALF grant 20150166), Swedish Society for Medical Research (T.G.), Guarantors of Brain (F.P.), ANR-10-COHO-002, OFSEP, EDMUS Foundation against multiple sclerosis, UK MS Society and the National Institute for Health Research University College London Hospitals Biomedical Research Centre, EMD Serono (C.M., R.B.)., ANR-10-COHO-0002,OFSEP,Observatoire Français de la Sclérose en Plaques(2010), Bannier, Elise, Cohortes - Observatoire Français de la Sclérose en Plaques - - OFSEP2010 - ANR-10-COHO-0002 - COHO - VALID, University of California [San Francisco] (UCSF), University of California-University of California, 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), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Assistance Publique - Hôpitaux de Marseille (APHM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), Eden, Dominique, Gros, Charley, Badji, Atef, Dupont, Sara M, De Leener, Benjamin, Maranzano, Josefina, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobia, Ouellette, Russell, Stawiarz, Leszek, Hillert, Jan, Talbott, Jason, Kerbrat, Anne, Edan, Gille, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A, Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Mario, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth A, Andrada Treaba, Constantina, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S, Nair, Govind, Shepherd, Timothy M, Charlson, Erik, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
0301 basic medicine, Adult, Male, medicine.medical_specialty, Cord, multicentre, Grey matter, multiple sclerosis, Spinal Cord Diseases, lesions, White matter, Lesion, 03 medical and health sciences, Disability Evaluation, 0302 clinical medicine, Multiple Sclerosis, Relapsing-Remitting, medicine, Humans, Gray Matter, 10. No inequality, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Spatial Analysis, Expanded Disability Status Scale, Clinically isolated syndrome, business.industry, Multiple sclerosis, Brain, Cervical Cord, spinal cord, Original Articles, Middle Aged, Multiple Sclerosis, Chronic Progressive, medicine.disease, Spinal cord, Magnetic Resonance Imaging, White Matter, 030104 developmental biology, medicine.anatomical_structure, Disease Progression, Female, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Neurology (clinical), Radiology, medicine.symptom, business, 030217 neurology & neurosurgery, MRI
Abstract

International audience; Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

Published
2019

3. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Author

Vincent Auclair, Ferran Prados, Jean-Christophe Brisset, Josefina Maranzano, Sridar Narayanan, Kouhei Kamiya, Constantina A. Treaba, Atef Badji, Hugh Kearney, Jean Pelletier, Pierre Labauge, Elise Bannier, Paola Valsasina, Erik Charlson, Daniel S. Reich, Olga Ciccarelli, Dominique Eden, Ren Zhuoquiong, Henitsoa Rasoanandrianina, Virginie Callot, Gilles Edan, Seth A. Smith, Marios C. Yiannakas, Jennifer Lefeuvre, Govind Nair, Allan R. Martin, Julien Cohen-Adad, Timothy M. Shepherd, Jason F. Talbott, Donald G. McLaren, Charley Gros, Masaaki Hori, Shahabeddin Vahdat, Julien Doyon, Yasuhiko Tachibana, Leszek Stawiarz, Shahamat Tauhid, Jan Hillert, Massimo Filippi, Lydia Chougar, Anne Kerbrat, Russell Ouellette, Caterina Mainero, Sara M. Dupont, Tobias Granberg, Bertrand Audoin, Ali Khatibi, Rohit Bakshi, Maria A. Rocca, Michael G. Fehlings, Benjamin De Leener, Yaou Liu, École Polytechnique de Montréal (EPM), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Department of Radiology and Biomedical Imaging [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), Xuanwu Hospital of Capital, Deutsche Telekom Group, Department of Computer Science [Liverpool], University of Liverpool, National Institute of Radiological Sciences (NIRS), Juntendo University Hospital [Tokyo], Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Karolinska Institutet [Stockholm], CHU Pontchaillou [Rennes], 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 d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de neurologie [Montpellier], Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Gui de Chauliac [CHU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université de Montpellier (UM), Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - AP-HM] (CEMEREM), Hôpital de la Timone [CHU - APHM] (TIMONE)-Centre de résonance magnétique biologique et médicale (CRMBM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS), Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Vita-Salute San Raffaele University and Center for Translational Genomics and Bioinformatics, Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), NMR Research Unit [London], Institute of Neurology [London], University College of London [London] (UCL)-University College of London [London] (UCL), University College of London [London] (UCL), Vanderbilt University [Nashville], Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], National Institutes of Health [Bethesda] (NIH), Biospective [Montréal], University of Toronto, Unité de Neuroimagerie Fonctionnelle [Montréal] (UNF-CRIUGM), Université de Montréal (UdeM)-Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Stanford University, McConnell Brain Imaging Centre (MNI), McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], New York University Langone Medical Center (NYU Langone Medical Center), NYU System (NYU), CIHR FDN-143263, Canadian Institutes of Health Research, International Society of Regulatory Toxicology and Pharmacology, Svenska Sällskapet för Medicinsk Forskning, IVADO, EDMUS Foundation, TransMedTech, R01 EY023240 (SAS), NIH/NEI, RG-1501-02840 (SAS), National Multiple Sclerosis Society, Intramural Research Program, Hospital Programme of Clinical Research (PHRC), NCT02117375, EMISEP project, Fondation A*midex-Investissements d'Avenir, 435897-2013, Natural Sciences and Engineering Research Council of Canada, Observatoire Français de la Sclérose en plaques (OFSEP), 13034, MOP, Canada Research Chairs, National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), 2015-PR-182754, Fonds de Recherche du Québec - Nature et Technologies, W81XWH-13-0073 (SAS), U.S. Department of Defense, ANR-10-COHO-002, Agence Nationale de la Recherche, R21 NS087465-01 (SAS), National Institute of Neurological Disorders and Stroke, Fondation Aix-Marseille Universite, SensoriMotor Rehabilitation Research Team (SMRRT), Wings for Life, 20150166, Stockholms Läns Landsting, Centre National de la Recherche Scientifique, 28826, Fonds de Recherche du Québec - Santé, 32454, Canada Foundation for Innovation, Centre d'Exploration Métabolique par Résonance Magnétique [Hôpital de la Timone - APHM] (CEMEREM), University of California [San Francisco] (UCSF), University of California-University of California, SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), 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 Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital Gui de Chauliac [Montpellier]-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université Montpellier 1 (UM1)-Université de Montpellier (UM), Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Centre National de la Recherche Scientifique (CNRS)- Hôpital de la Timone [CHU - APHM] (TIMONE), Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Gros, Charley, De Leener, Benjamin, Badji, Atef, Maranzano, Josefina, Eden, Dominique, Dupont, Sara M., Talbott, Jason, Zhuoquiong, Ren, Liu, Yaou, Granberg, Tobia, Ouellette, Russell, Tachibana, Yasuhiko, Hori, Masaaki, Kamiya, Kouhei, Chougar, Lydia, Stawiarz, Leszek, Hillert, Jan, Bannier, Elise, Kerbrat, Anne, Edan, Gille, Labauge, Pierre, Callot, Virginie, Pelletier, Jean, Audoin, Bertrand, Rasoanandrianina, Henitsoa, Brisset, Jean-Christophe, Valsasina, Paola, Rocca, Maria A., Filippi, Massimo, Bakshi, Rohit, Tauhid, Shahamat, Prados, Ferran, Yiannakas, Mario, Kearney, Hugh, Ciccarelli, Olga, Smith, Seth, Treaba, Constantina Andrada, Mainero, Caterina, Lefeuvre, Jennifer, Reich, Daniel S., Nair, Govind, Auclair, Vincent, Mclaren, Donald G., Martin, Allan R., Fehlings, Michael G., Vahdat, Shahabeddin, Khatibi, Ali, Doyon, Julien, Shepherd, Timothy, Charlson, Erik, Narayanan, Sridar, and Cohen-Adad, Julien

Subjects
FOS: Computer and information sciences, Image Processing, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Neurodegenerative, computer.software_genre, Convolutional neural network, Medical and Health Sciences, Pattern Recognition, Automated, 0302 clinical medicine, Computer-Assisted, Segmentation, Voxel, Image Processing, Computer-Assisted, Medicine, Spinal Cord Injury, Observer Variation, Spinal cord, medicine.diagnostic_test, 05 social sciences, Magnetic Resonance Imaging, 3. Good health, medicine.anatomical_structure, Neurology, Neurological, Biomedical Imaging, Convolutional neural networks, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Radiology, medicine.symptom, MRI, Automated, medicine.medical_specialty, Cord, Multiple Sclerosis, Physical Injury - Accidents and Adverse Effects, Neural Networks, Cognitive Neuroscience, Bioengineering, Pattern Recognition, Sensitivity and Specificity, Autoimmune Disease, 050105 experimental psychology, Article, Lesion, Multiple sclerosis, 03 medical and health sciences, Computer, Humans, 0501 psychology and cognitive sciences, Multiple sclerosi, Traumatic Head and Spine Injury, Neurology & Neurosurgery, business.industry, Psychology and Cognitive Sciences, Neurosciences, Reproducibility of Results, Magnetic resonance imaging, medicine.disease, Brain Disorders, Neural Networks, Computer, business, computer, 030217 neurology & neurosurgery
Abstract

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg, a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. The proposed framework is open-source and readily available in the Spinal Cord Toolbox., Comment: 38 pages, 7 figures, 2 tables

Published
2019

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