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

Olivier Commowick 1 Audrey Istace 2 Michael Kain 1 Baptiste Laurent 3 Florent Leray 1 Mathieu Simon 1 Sorina Pop 4 Pascal Girard 4 Roxana Ameli 2 Jean-Christophe Ferré 5, 1 Anne Kerbrat 6, 1 Thomas Tourdias 7 Frédéric Cervenansky 8 Tristan Glatard 9 Jeremy Beaumont 1 Senan Doyle 10 Florence Forbes 11 Jesse Knight 12 April Khademi 13 Amirreza Mahbod 14 Chunliang Wang 14 Richard Mckinley 15 Franca Wagner 15 John Muschelli 16 Elizabeth Sweeney 16 Eloy Roura 17 Xavier Lladó 17 Michel Santos 18 Wellington Santos 18 Abel Silva-Filho 18 Xavier Tomas-Fernandez 19 Hélène Urien 20 Isabelle Bloch 20 Sergi Valverde 17 Mariano Cabezas 17 Francisco Vera-Olmos 21 Norberto Malpica 21 Charles Guttmann 22 Sandra Vukusic 2 Gilles Edan 1, 23 Michel Dojat 24 Martin Styner 25 Simon Warfield 19 François Cotton 2 Christian Barillot 1, *
* Auteur correspondant
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U1228, Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
8 Service Informatique et développements
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
11 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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-the-art 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.
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Scientific Reports, Nature Publishing Group, 2018, 8, pp.13650. 〈10.1038/s41598-018-31911-7〉
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Olivier Commowick, Audrey Istace, Michael Kain, Baptiste Laurent, Florent Leray, et al.. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. Scientific Reports, Nature Publishing Group, 2018, 8, pp.13650. 〈10.1038/s41598-018-31911-7〉. 〈inserm-01847873v2〉

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