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1. Deep-learning based segmentation of challenging myelin sheaths

Author

Raphael Caillon, Anne Joutel, Florence Rossant, Hélène Urien, Rikesh M. Rajani, Thomas Le Couedic, Institut Supérieur d'Electronique de Paris (ISEP), Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm - Paris Descartes), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Rossant, Florence

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], g- ratio, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Corpus callosum, 030218 nuclear medicine & medical imaging, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Segmentation, axon, convolutional neural network (CNN), electron microscopy, business.industry, Deep learning, Myelin sheaths, segmentation, deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Data set, myelin, White Matter Diseases, Artificial intelligence, business, Encoder, 030217 neurology & neurosurgery
Abstract

International audience; The segmentation of axons and myelin in electron microscopy images allows neurologists to highlight the density of axons and the thickness of the myelin surrounding them. These properties are of great interest for preventing and anticipating white matter diseases. This task is generally performed manually, which is a long and tedious process. We present an update of the methods used to compute that segmentation via machine learning. Our model is based on the architecture of the U-Net network. Our main contribution consists in using transfer learning in the encoder part of the U-Net network, as well as test time augmentation when segmenting. We use the SE-Resnet50 backbone weights which was pre-trained on the ImageNet 2012 dataset. We used a data set of 23 images with the corresponding segmented masks, which also was challenging due to its extremely small size. The results show very encouraging performances compared to the state-of-the-art with an average precision of 92% on the test images. It is also important to note that the available samples were taken from elderly mices in the corpus callosum. This represented an additional difficulty, compared to related works that had samples taken from the spinal cord or the optic nerve of healthy individuals, with better contours and less debris.

Published
2020

2. 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

3. The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities

Author

Alessio Virzi, Yongchao Xu, Isabelle Bloch, Catherine Adamsbaum, Sonia Dahdouh, Hélène Urien, Baptiste Morel, Elodie Puybareau, Thierry Géraud, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Laboratoire de Recherche et de Développement de l'EPITA (LRDE), Ecole Pour l'Informatique et les Techniques Avancées (EPITA), Service de Radiologie [CHU Trousseau], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Université Francois Rabelais [Tours], Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Service d'Imagerie Pédiatrique, AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Université Paris-Sud - Paris 11 (UP11), and Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)

Subjects
preterm brain MRI, max-tree representation, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, Population, semi-automatic tissue segmentation, Health Informatics, Neuroimaging, Mathematical morphology, Neonatal brain MRI, 030218 nuclear medicine & medical imaging, White matter, 03 medical and health sciences, 0302 clinical medicine, Image Interpretation, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Humans, mathematical morphology, Radiology, Nuclear Medicine and imaging, Segmentation, education, education.field_of_study, High signal intensity, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Infant, Newborn, Brain, Magnetic resonance imaging, Pattern recognition, white matter hyperintensities, Computer Graphics and Computer-Aided Design, Magnetic Resonance Imaging, White Matter, Hyperintensity, medicine.anatomical_structure, Homogeneous, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Computer Vision and Pattern Recognition, Artificial intelligence, business, 030217 neurology & neurosurgery, Infant, Premature
Abstract

International audience; Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi-automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max-tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.5T, which corresponds to usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized.

Published
2017

4. Brain lesion detection in 3D PET images using max-trees and a new spatial context criterion

Author

Isabelle Bloch, Hélène Urien, Nicolas Rougon, Irène Buvat, Michael Soussan, Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, ANR-12-CORD-0017,LOGIMA,Logiques, représentations structurées, morphologie mathématique et incertain pour l'interprétation sémantique d'images et de vidéos(2012), HAL, TelecomParis, and Contenus et Interactions - Logiques, représentations structurées, morphologie mathématique et incertain pour l'interprétation sémantique d'images et de vidéos - - LOGIMA2012 - ANR-12-CORD-0017 - CONTINT - VALID

Subjects
Spatial contextual awareness, medicine.diagnostic_test, Computer science, business.industry, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 020206 networking & telecommunications, 02 engineering and technology, Detection, Predictive value, Brain tumors, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Positron emission tomography, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Max-tree representation, 0202 electrical engineering, electronic engineering, information engineering, medicine, Spatial context, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Brain lesions, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Positron Emission Tomography
Abstract

International audience; In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for \textsuperscript{18}$F$-FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this criterion is proposed, and was evaluated on five patients with brain metastases and tuberculosis, and quantitatively assessed using the true positive rates and positive predictive values. The experimental results show that the method detects all the lesions in the PET.

Published
2017

5. 3D PET-driven multi-phase segmentation of meningiomas in MRI

Author

Sarah Boughdad, Irène Buvat, Hélène Urien, I. Block, and Nicolas Rougon

Subjects
PET-CT, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Magnetic resonance spectroscopic imaging, Magnetic resonance imaging, Context (language use), 02 engineering and technology, Real-time MRI, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Positron emission tomography, Dynamic contrast-enhanced MRI, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Radiology, business, Nuclear medicine, Preclinical imaging
Abstract

Combining anatomical and functional information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans brings great opportunities to improve diagnosis in oncology and treatment planning in radiation oncology. In this work, we propose a PET-guided MR tumor segmentation method minimizing a globally convex energy in a multiphase framework to account for the context variability of lesions. The method was evaluated in four patients with atypical meningiomas of different shapes, locations and metabolism, and the Dice index obtained by comparing with a manual tumor segmentation performed by an expert was 0.65 ± 0.13. The results demonstrated a good ability of the method to differentiate tumors from tissues with similar MRI intensity values.

Published
2016

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