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1. Automated Quantification of Brain Lesion Volume From Post-trauma MR Diffusion-Weighted Images

Author

Thomas Mistral, Pauline Roca, Christophe Maggia, Alan Tucholka, Florence Forbes, Senan Doyle, Alexandre Krainik, Damien Galanaud, Emmanuelle Schmitt, Stéphane Kremer, Adrian Kastler, Irène Troprès, Emmanuel L. Barbier, Jean-François Payen, and Michel Dojat

Subjects
MRI, mean diffusion (MD), segmentation (image processing), traumatic brain injury, brain, Neurology. Diseases of the nervous system, RC346-429
Abstract

ObjectivesDetermining the volume of brain lesions after trauma is challenging. Manual delineation is observer-dependent and time-consuming and cannot therefore be used in routine practice. The study aimed to evaluate the feasibility of an automated atlas-based quantification procedure (AQP) based on the detection of abnormal mean diffusivity (MD) values computed from diffusion-weighted MR images.MethodsThe performance of AQP was measured against manual delineation consensus by independent raters in two series of experiments based on: (i) realistic trauma phantoms (n = 5) where low and high MD values were assigned to healthy brain images according to the intensity, form and location of lesion observed in real TBI cases; (ii) severe TBI patients (n = 12 patients) who underwent MR imaging within 10 days after injury.ResultsIn realistic TBI phantoms, no statistical differences in Dice similarity coefficient, precision and brain lesion volumes were found between AQP, the rater consensus and the ground truth lesion delineations. Similar findings were obtained when comparing AQP and manual annotations for TBI patients. The intra-class correlation coefficient between AQP and manual delineation was 0.70 in realistic phantoms and 0.92 in TBI patients. The volume of brain lesions detected in TBI patients was 59 ml (19–84 ml) (median; 25–75th centiles).ConclusionsOur results support the feasibility of using an automated quantification procedure to determine, with similar accuracy to manual delineation, the volume of low and high MD brain lesions after trauma, and thus allow the determination of the type and volume of edematous brain lesions. This approach had comparable performance with manual delineation by a panel of experts. It will be tested in a large cohort of patients enrolled in the multicenter OxyTC trial (NCT02754063).

Published
2022
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14. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

Author

Bjoern H. Menze, András Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin S. Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth R. Gerstner, Marc-André Weber, Tal Arbel, Brian B. Avants, Nicholas Ayache, Patricia Buendia, D. Louis Collins, Nicolas Cordier, Jason J. Corso, Antonio Criminisi, Tilak Das, Herve Delingette, çagatay Demiralp, Christopher R. Durst, Michel Dojat, Senan Doyle, Joana Festa, Florence Forbes, Ezequiel Geremia, Ben Glocker, Polina Golland, Xiaotao Guo, Andac Hamamci, Khan M. Iftekharuddin, Raj Jena, Nigel M. John, Ender Konukoglu, Danial Lashkari, José Antonio Mariz, Raphael Meier, Sérgio Pereira, Doina Precup, Stephen J. Price, Tammy Riklin Raviv, Syed M. S. Reza, Michael T. Ryan, Duygu Sarikaya, Lawrence H. Schwartz, Hoo-Chang Shin, Jamie Shotton, Carlos A. Silva 0002, Nuno J. Sousa, Nagesh K. Subbanna, Gábor Székely, Thomas J. Taylor, Owen M. Thomas, Nicholas J. Tustison, Gözde B. ünal, Flor Vasseur, Max Wintermark, Dong Hye Ye, Liang Zhao 0018, Binsheng Zhao, Darko Zikic, Marcel Prastawa, Mauricio Reyes 0001, and Koen Van Leemput

Published
2015
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18. Automatic quantification of brain lesion volume from post-trauma MR Images

Author

Jean-François Payen, Alexandre Krainik, Kremer S, Christophe Maggia, Irène Troprès, Senan Doyle, Schmitt E, Thomas Mistral, Michel Dojat, Emmanuel L. Barbier, Alan Tucholka, P Roca, Adrian Kastler, Galanaud D, Florence Forbes, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Pixyl Medical [Grenoble], Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), 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 Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), IRMaGe (IRMaGe), CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Les Hôptaux universitaires de Strasbourg (HUS), and CHU Strasbourg

Subjects
business.industry, Traumatic brain injury, [SDV]Life Sciences [q-bio], Statistical difference, medicine.disease, Mr imaging, 030218 nuclear medicine & medical imaging, Lesion, 03 medical and health sciences, 0302 clinical medicine, Text mining, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Medicine, Brain lesions, Mr images, medicine.symptom, business, Nuclear medicine, 030217 neurology & neurosurgery, Volume (compression)
Abstract

ObjectivesThe determination of the volume of brain lesions after trauma is challenging. Manual delineation is observer-dependent and time-consuming which inhibits the practice in clinical routine. We propose and evaluate an automated atlas-based quantification procedure (AQP) based on the detection of abnormal mean diffusivity (MD) values computed from diffusion-weighted MR images.MethodsWe measured the performance of AQP versus manual delineation consensus by independent raters in two series of experiments: i) realistic trauma phantoms (n=5) where abnormal MD values were assigned to healthy brain images according to the intensity, form and location of lesion observed in real TBI cases; ii) severe TBI patients (n=12 patients) who underwent MR imaging within 10 days after injury.ResultsIn realistic trauma phantoms, no statistical difference in Dice similarity coefficient, precision and brain lesion volumes was found between AQP, the rater consensus and the ground truth lesion delineations. Similar findings were obtained when comparing AQP and manual annotations for TBI patients. The intra-class correlation coefficient between AQP and manual delineation was 0.70 in realistic phantoms and 0.92 in TBI patients. The volume of brain lesions detected in TBI patients was 59 ml (19-84 ml) (median; 25-75th centiles).Conclusionsour results indicate that an automatic quantification procedure could accurately determine with accuracy the volume of brain lesions after trauma. This presents an opportunity to support the individualized management of severe TBI patients.Key pointsThe management of patients with severe traumatic brain injury is complex, and access to objective quantitative information lesion volumes can support clinical decision-making.An automated delineation procedure was developed to determine the nature and volume of brain lesions post-trauma.This procedure was based on diffusion weighted MR-imaging to quantify the volume of vasogenic and cellular edema from realistic phantoms and patients with severe traumatic brain injury.Nature and quantification of the brain lesions volume compared favorably with manual delineation of brain lesions by a panel of experts.

Published
2021

19. Focal cortical atrophy following transient meningeal enhancement in a progressive multiple sclerosis

Author

Lise-Marlène Puvilland, Stéphanie Demasles, Alan Tucholka, Mickael Bonnan, Camille Dahan, Elsa Krim, Paul Money, Senan Doyle, Bruno Barroso, Pauline Desblache, and Raluca Marasescu

Subjects
Pathology, medicine.medical_specialty, Neurology, Multiple Sclerosis, Inflammation, Dermatology, Fluid-attenuated inversion recovery, 03 medical and health sciences, 0302 clinical medicine, Meninges, Medicine, Humans, 030212 general & internal medicine, Neuroradiology, Cortical atrophy, Cerebral Cortex, business.industry, Multiple sclerosis, General Medicine, Multiple Sclerosis, Chronic Progressive, medicine.disease, Magnetic Resonance Imaging, Hyperintensity, Psychiatry and Mental health, Neurology (clinical), Neurosurgery, medicine.symptom, Atrophy, business, 030217 neurology & neurosurgery
Abstract

Recent studies identified chronic leptomeningeal enhancement (LME) in late-acquired FLAIR sequences in secondary progressive (SP) multiple sclerosis (MS). These LMEs correlate with focal cortical inflammation and demyelination observed by pathology, which are supposed to drive long-term cortical atrophy. We report a spontaneously remitting meningeal uptake in a patient suffering from SP MS. No cortical lesion was visible on FLAIR or DIR sequences, but the rate of cortical atrophy was higher in this area. This case suggests that conventional 3-T MRI, by contrary to white matter lesions, may be amnesic with regard to the potential burden of previous regressive meningeal lesions. Moreover, T1-enhanced sequences underscore the real inflammatory activity. LME could be more than passive markers of SP MS, but is also directly responsible for focal cortical atrophy and could be an early manifestation of cortical lesions.

Published
2020

20. 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

21. Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation

Author

Olivier Detante, Senan Doyle, Olivier Heck, Assia Jaillard, Florence Forbes, and Michel Dojat

Subjects
medicine.diagnostic_test, Computer science, Atlas (topology), business.industry, 05 social sciences, Probabilistic logic, Pattern recognition, Magnetic resonance imaging, Sub acute, Fluid-attenuated inversion recovery, 050105 experimental psychology, Lesion, 03 medical and health sciences, 0302 clinical medicine, Ischemic stroke, medicine, 0501 psychology and cognitive sciences, Artificial intelligence, medicine.symptom, Hidden Markov random field, business, 030217 neurology & neurosurgery
Abstract

Automatic segmentation of chronic stroke lesion from magnetic resonance images (MRI) is motivated by the increasing need for reproducible and repeatable endpoints in clinical trials. The task is non-trivial, due to a number of confounding factors, including heterogeneous lesion intensity, irregular shape, and large deformations that render the conventional use of prior probabilistic atlases challenging. In this paper, we introduce a hidden Markov random field model that avails of a novel prior probabilistic vascular territory atlas to describe the natural vascular constraints in the brain. The vascular territory atlas is deformed in a joint registration-segmentation framework to overcome subject-specific morphological variability. T1-w and Flair sequences are used to populate our model, and a variational approach is implemented to find a solution. The performance of our model is demonstrated on two datasets, and compared to manual delineations by expert raters.

Published
2018

22. Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes

Author

Emmanuelle Schmitt, Alexandre Krainik, Jean-François Payen, Thomas Mistral, Michel Dojat, Florence Forbes, Stéphane Kremer, Senan Doyle, Damien Galanaud, Irène Troprès, Emmanuel L. Barbier, Christophe Maggia, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Université Grenoble Alpes - UFR Médecine (UGA UFRM), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), 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)-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]), Service de Neuroradiologie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], 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 Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Service de radiologie [Strasbourg], CHU Strasbourg-Hôpital de Hautepierre [Strasbourg], RMN biomédicale : de la cellule à l'homme (RBCH), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble-DIR CENTRALE DU SSA-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), IRMaGe (IRMaGe ), CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Forbes, Florence

Subjects
Traumatic brain injury, business.industry, Computer science, Brain tissue, medicine.disease, Thermal diffusivity, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], medicine, Brain lesions, Nuclear medicine, business, Mr diffusion, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Brain trauma, 030217 neurology & neurosurgery, Automated method
Abstract

International audience; We report the evaluation of an automated method for quantification of brain tissue damage, caused by a severe traumatic brain injury, using mean diffusivity computed from MR diffusion images. Our automatic results obtained on realistic phantoms and real patient images 10 days post-event provided by nine different centers were coherent with four expert manually identified lesions. For realistic phantoms automated method scores were equal to 0.77, 0.77 and 0.83 for Dice, Precision and Sensibility respectively compared to 0.78, 0.72 and 0.86 for the experts. The inter correlation class (ICC) was 0.79. For 7/9 real cases 0.57, 0.50 and 0.70 were respectively obtained for automated method compared to 0.60, 0.52 and 0.78 for experts with ICC=0.71. Additionally, we detail the quality control module used to pool data from various image provider centers. This study clearly demonstrates the validity of the proposed automated method to eventually compute in a multi-centre project, the lesional load following brain trauma based on MD changes.

Published
2018

23. Atrophie corticale focale après une poussée méningée dans une sclérose en plaques progressive

Author

Senan Doyle, Bruno Barroso, Alan Tucholka, Mickael Bonnan, Pauline Desblache, and Paul Money

Subjects
Neurology, Neurology (clinical)
Abstract

Introduction Les lesions corticales sous-piales de la sclerose en plaques (SEP) sont associees a des prises de contraste leptomeningees (PCLM), mises en evidence exclusivement par acquisition de sequences IRM FLAIR tardives. Objectifs Demontrer l’apparition d’une atrophie corticale focale en regard des PCLM chez une patiente atteinte de SEP secondaire progressive et presentant une telle lesion. Patients et methodes Acquisitions de points temporels IRM 3 T a 0, 3 et 9 mois comportant des sequences classiques, volumetriques et FLAIR-gado tardives (> 20 min). Deux regions d’interet ont ete definies sur les coupes axiales : le cortex adjacent a la PCLM, et le cortex a distance ; une marge de securite (3 mm) est conservee entre les volumes. L’epaisseur corticale etait extraite pour chaque point temporel (Pixyl Neuro software) et les resultats sont exprimes en pourcentage de changement depuis le debut. Resultats Aucune manifestation clinique n’etait associee a cette PCLM qui a spontanement disparu au troisieme mois. Aucun traitement de la SEP n’a ete administre durant cette periode. Les sequences FLAIR et DIR corticales etaient normales en regard de la PCLM. L’atrophie corticale etait significativement superieure dans le cortex PCLM pour tous les points temporels : a 3 mois − 2,74 % vs + 0,55 % et a 9 mois : − 4,90 % vs − 0,98 % (p Discussion Les PCLM sont caracteristiques de la phase progressive. Nous apportons la premiere demonstration d’une correlation entre PCLM et atrophie corticale. Notre resultat suggere : – que l’IRM conventionnelle sous-estime l’activite inflammatoire ; – que les poussees PCLM laissent des sequelles anatomiques atrophiques ; – que l’IRM conventionnelle FLAIR et DIR est amnesique de ces poussees. Conclusion Les sequences Flair-gado tardives apportent une information neuve. Ce resultat suggere que des poussees meningees infra-radiologiques pourraient sous-tendre tout ou partie de la forme progressive de SEP.

Published
2019

24. Assessment of Tissue Injury in Severe Brain Trauma

Author

Corentin Berthet, Christophe Maggia, Irène Troprès, Senan Doyle, Yann Teyssier, Olivier Heck, Michel Dojat, Florence Forbes, Jean François Payen, Lionel Velly, Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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)-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]), Neuro-imagerie fonctionnelle et métabolique (ANTE-INSERM U836, équipe 5), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Forbes, Florence, [GIN] Grenoble Institut des Neurosciences (GIN), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
medicine.medical_specialty, Traumatic brain injury, business.industry, [SDV]Life Sciences [q-bio], Hemorrhagic lesion, medicine.disease, computer.software_genre, 3. Good health, Lesion load, [SDV] Life Sciences [q-bio], Voxel, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Tissue damage, medicine, Effective diffusion coefficient, Radiology, business, Brain trauma, computer, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Diffusion MRI
Abstract

International audience; We report our methodological developments to investigate, in a multi-center study using mean diffusivity, the tissue damage caused by a severe traumatic brain injury (GSC

Published
2016

25. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Author

Nicholas Ayache, Danial Lashkari, Yuliya Burren, Polina Golland, Duygu Sarikaya, Levente Lanczi, Florence Forbes, Binsheng Zhao, Michel Dojat, Brian B. Avants, Marcel Prastawa, Liang Zhao, Andac Hamamci, Jamie Shotton, Ben Glocker, Thomas J. Taylor, Joana Festa, Nicolas Cordier, Hervé Delingette, Johannes Slotboom, Doina Precup, Antonio Criminisi, Ezequiel Geremia, Lawrence H. Schwartz, Stephen J. Price, Gábor Székely, Carlos A. Silva, José Mariz, Owen M. Thomas, Tilak Das, Ender Konukoglu, Dong Hye Ye, Khan M. Iftekharuddin, Syed M. S. Reza, Xiaotao Guo, Koen Van Leemput, Raphael Meier, Senan Doyle, Christopher R. Durst, D. Louis Collins, Raj Jena, Tal Arbel, Elizabeth R. Gerstner, Darko Zikic, Çağatay Demiralp, Marc-André Weber, Keyvan Farahani, Nagesh K. Subbanna, Stefan Bauer, Nicholas J. Tustison, Sérgio Pereira, Bjoern H. Menze, Patricia Buendia, Jason J. Corso, Mauricio Reyes, Nuno Sousa, Max Wintermark, Jayashree Kalpathy-Cramer, Flor Vasseur, Justin Kirby, Gozde Unal, Andras Jakab, Roland Wiest, Tammy Riklin Raviv, Nigel M. John, N Porz, Michael Ryan, Hoo-Chang Shin, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational Image Analysis and Radiology (CIR lab), Medizinische Universität Wien = Medical University of Vienna, Institute for Surgical Technology and Biomechanics [Bern] (ISTB), University of Bern, Massachusetts General Hospital [Boston], Department of Health and Human Services, National Institutes of Health [Bethesda] (NIH), Inselspital Bern, University of Debrecen Egyetem [Debrecen], Division of Medical Physics in Radiology [Heidelberg], German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Diagnostic and Interventional Radiology [Heidelberg], Heidelberg University Hospital [Heidelberg], Centre for Intelligent Machines (CIM), McGill University = Université McGill [Montréal, Canada], Penn Image Computing & Science Lab [Philadelphia] (PICSL), University of Pennsylvania, INFOTECH Soft, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], State University of New York (SUNY), Microsoft Research [Cambridge] (Microsoft), Microsoft Research, Cambridge University Hospitals - NHS (CUH), University of Cambridge [UK] (CAM), Computer Science Department [Stanford], Stanford University, Department of radiology and medical imaging [Charlottesville], University of Virginia, Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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 (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Universidade do Minho = University of Minho [Braga], Computer Science and Artificial Intelligence Laboratory [Cambridge] (CSAIL), Massachusetts Institute of Technology (MIT), Columbia University [New York], Faculty of Engineering and Natural Sciences (Sabanci University), Sabanci University [Istanbul], Old Dominion University [Norfolk] (ODU), Addenbrooke's Hospital, Cambridge University NHS Trust, Bangor University, Department of Surgery, Université de Genève = University of Geneva (UNIGE), Bristol Glaciology Centre, School of Geographical Sciences, Molecular Carcinogenesis [Sutton], Institute of cancer research, Center for Neuroscience and Cell Biology (CNC) (CNC), University of Coimbra [Portugal] (UC)-Neuroscience Research Domain, Computer Vision Laboratory - ETHZ [Zurich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Department of Radiology [San Francisco], University of California [San Francisco] (UC San Francisco), University of California (UC)-University of California (UC), Purdue University [West Lafayette], Department of Computer Science [New York], Scientific Computing and Imaging Institute (SCI Institute), University of Utah, Department of radiology (Massachusetts General Hospital), European Project: 291080,EC:FP7:ERC,ERC-2011-ADG_20110209,MEDYMA(2012), Menze, Bjoern, Biophysical Modeling and Analysis of Dynamic Medical Images. - MEDYMA - - EC:FP7:ERC2012-04-01 - 2017-03-31 - 291080 - VALID, [et al.], University of Pennsylvania [Philadelphia], University of Virginia [Charlottesville], Universidade do Minho, University of Geneva [Switzerland], University of California [San Francisco] (UCSF), University of California-University of California, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Golland, Polina, and Lashkari, Danial

Subjects
Computer science, Oncology/tumor, Brain tumor, [SCCO.COMP]Cognitive science/Computer science, Neuroimaging, Benchmark, computer.software_genre, Article, Set (abstract data type), [SCCO.COMP] Cognitive science/Computer science, Cut, medicine, Medical imaging, Humans, Electrical and Electronic Engineering, Image segmentation, Science & Technology, Radiological and Ultrasound Technology, business.industry, Brain, Pattern recognition, Glioma, medicine.disease, Magnetic Resonance Imaging, 3. Good health, Computer Science Applications, Benchmarking, Benchmark (computing), Data mining, Artificial intelligence, business, computer, Algorithms, Software, MRI
Abstract

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource., National Center for Research Resources (U.S.) (P41-RR14075), National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB013565), National Institute of Biomedical Imaging and Bioengineering (U.S.) (U54-EB005149), National Institutes of Health (U.S.) (P41-RR13218), National Institute of Biomedical Imaging and Bioengineering (U.S.) (P41-EB-015902), National Cancer Institute (U.S.) (R15CA115464)

Published
2014

26. On the difficulty to delimit disease risk hot spots

Author

Senan Doyle, Myriam Charras-Garrido, Florence Forbes, David Abrial, Lamiae Azizi, Nicolas Peyrard, Unité d'épidémiologie animale, Institut National de la Recherche Agronomique (INRA), 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), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Unité de recherche d'Épidémiologie Animale (UEA), Institut National de Recherche en Informatique et en Automatique (Inria), and Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA)

Subjects
Scan statistic, Epidemiology, MODELS, BOVINE SPONGIFORM ENCEPHALOPATHY, MARKOV RANDOM-FIELDS, Disease, SPATIAL-ANALYSIS, Management, Monitoring, Policy and Law, Machine learning, computer.software_genre, 01 natural sciences, BSE, 010104 statistics & probability, 03 medical and health sciences, EM ALGORITHM, Expectation–maximization algorithm, [INFO]Computer Science [cs], 0101 mathematics, Computers in Earth Sciences, [MATH]Mathematics [math], Cluster analysis, 030304 developmental biology, Earth-Surface Processes, 0303 health sciences, Global and Planetary Change, IMAGE SEGMENTATION, business.industry, [SDV.BA]Life Sciences [q-bio]/Animal biology, Generalized Potts model, Classification, Spatial clustering, Grouped data, FIELD-LIKE APPROXIMATIONS, Geography, Disease risk, Disease mapping, Data mining, Artificial intelligence, Hidden Markov random field, CLUSTERS, Focus (optics), business, computer
Abstract

Representing the health state of a region is a helpful tool to highlight spatial heterogeneity and localize high risk areas. For ease of interpretation and to determine where to apply control procedures, we need to clearly identify and delineate homogeneous regions in terms of disease risk, and in particular disease risk hot spots. However, even if practical purposes require the delineation of different risk classes, such a classification does not correspond to a reality and is thus difficult to estimate. Working with grouped data, a first natural choice is to apply disease mapping models. We apply a usual disease mapping model, producing continuous estimations of the risks that requires a post-processing classification step to obtain clearly delimited risk zones. We also apply a risk partition model that build a classification of the risk levels in a one step procedure. Working with point data, we will focus on the scan statistic clustering method. We illustrate our article with a real example concerning the bovin spongiform encephalopathy (BSE) an animal disease whose zones at risk are well known by the epidemiologists. We show that in this difficult case of a rare disease and a very heterogeneous population, the different methods provide risk zones that are globally coherent. But, related to the dichotomy between the need and the reality, the exact delimitation of the risk zones, as well as the corresponding estimated risks are quite different. (C) 2012 Elsevier B.V. All rights reserved.

Published
2012

27. Risk mapping based on hidden Markov random field and variational approximations

Author

Azizi, Lamiae, Forbes, Florence, Senan, Doyle, Charras-Garrido, Myriam, Abrial, David, Unité de recherche d'Épidémiologie Animale (UEA), Institut National de la Recherche Agronomique (INRA), Institut National de Recherche en Informatique et en Automatique (Inria), and Ville service.

Subjects
risk mapping, [SDV]Life Sciences [q-bio], approximation, markov
Published
2011

28. Spatial risk mapping for rare disease with hidden Markov fields and variational EM

Author

Senan Doyle, Lamiae Azizi, David Abrial, Florence Forbes, Myriam Charras-Garrido, Unité d'épidémiologie animale, Institut National de la Recherche Agronomique (INRA), 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), INRIA, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria), and Unité de recherche d'Épidémiologie Animale (UEA)

Subjects
FOS: Computer and information sciences, Computer science, [SDV]Life Sciences [q-bio], Initialization, Variation (game tree), Poisson distribution, 01 natural sciences, BSE, 010104 statistics & probability, 0302 clinical medicine, Risk analysis (business), [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], FEED, Variational EM, Potts model, 030212 general & internal medicine, Hidden Markov model, approximation, markov, education.field_of_study, [STAT.AP]Statistics [stat]/Applications [stat.AP], VALUES, Classification, discrete hidden Markov random field, disease mapping, variational EM, BOVINE SPONGIFORM ENCEPHALOPATHY, ALGORITHM, FEED, LIKELIHOOD, FRANCE, BAN, BSE, MIXTURE-MODELS, Classification, Modeling and Simulation, symbols, Poisson mixtures, Potts model, MIXTURE-MODELS, APPROXIMATIONS, VALUES, Discrete hidden Markov random field, Disease mapping, Statistics, Probability and Uncertainty, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Statistics and Probability, Risk analysis, ACM: G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS, Population, BOVINE SPONGIFORM ENCEPHALOPATHY, FRANCE, APPROXIMATIONS, Statistics - Applications, LIKELIHOOD, 03 medical and health sciences, symbols.namesake, Expectation–maximization algorithm, Applications (stat.AP), ALGORITHM, 0101 mathematics, education, Poisson mixtures, BAN, risk mapping, business.industry, Pattern recognition, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Artificial intelligence, Focus (optics), business
Abstract

Current risk mapping models for pooled data focus on the estimated risk for each geographical unit. A risk classification, that is, grouping of geographical units with similar risk, is then necessary to easily draw interpretable maps, with clearly delimited zones in which protection measures can be applied. As an illustration, we focus on the Bovine Spongiform Encephalopathy (BSE) disease that threatened the bovine production in Europe and generated drastic cow culling. This example features typical animal disease risk analysis issues with very low risk values, small numbers of observed cases and population sizes that increase the difficulty of an automatic classification. We propose to handle this task in a spatial clustering framework using a nonstandard discrete hidden Markov model prior designed to favor a smooth risk variation. The model parameters are estimated using an EM algorithm and a mean field approximation for which we develop a new initialization strategy appropriate for spatial Poisson mixtures. Using both simulated and our BSE data, we show that our strategy performs well in dealing with low population sizes and accurately determines high risk regions, both in terms of localization and risk level estimation., Published in at http://dx.doi.org/10.1214/13-AOAS629 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2011

29. Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation

Author

Christian Barillot, Florence Forbes, Senan Doyle, Daniel García-Lorenzo, Michel Dojat, Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), 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), 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)-Télécom Bretagne-CentraleSupélec-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Grenoble Institut des Neurosciences (GIN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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 (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Barillot, Christian

Subjects
Brain modeling, Bayesian methods, Computer science, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Markov process, Markov model, Bayesian inference, 030218 nuclear medicine & medical imaging, Multiple sclerosis, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Magnetic resonance imaging, Markov random fields, Robustness (computer science), medicine, Pathology, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Segmentation, Computer vision, Robustness, [SDV.IB] Life Sciences [q-bio]/Bioengineering, medicine.diagnostic_test, business.industry, State-space methods, Pattern recognition, Image segmentation, Magnetic resonance, symbols, Lesions, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract

International audience; We propose a technique for fusing the output of multiple Magnetic Resonance (MR) sequences to robustly and accurately segment brain lesions. It is based on a Bayesian multi-sequence Markov model that includes weight parameters to account for the relative importance and control the impact of each sequence. The Bayesian framework has the advantage of allowing 1) the incorporation of expert knowledge on the a priori relevant information content of each sequence and 2) a weighting scheme which is modified adaptively according to the data and the segmentation task under consideration. The model, applied to the detection of multiple sclerosis and stroke lesions shows promising results.

Published
2010

30. Feasible Link Statistics for Adaptive Ad Hoc Networks

Author

Senan Doyle, Linda Doyle, and Tim Forde

Subjects
Stochastic geometry models of wireless networks, Optimized Link State Routing Protocol, Adaptive quality of service multi-hop routing, Computer science, Wireless ad hoc network, Statistics, Probability distribution, Network performance, Mobile ad hoc network, Ad hoc wireless distribution service
Abstract

The dynamic nature of ad hoc networks advocates the use of adaptive schemes to optimize network performance. Such adaptive schemes require local observations of prevailing network conditions. This paper discusses the dependencies between locally observable metrics. The probability density functions for link statistic metrics are assessed, and the implications of the distribution model for parametric inference is investigated.

Published
2006

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