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

Olivier Commowick; Audrey Istace; Michael Kain; Baptiste Laurent; Florent Leray; Mathieu Simon; Sorina Camarasu Pop; Pascal Girard; Roxana Ameli; Jean-Christophe Ferré; Anne Kerbrat; Thomas Tourdias; Frédéric Cervenansky; Tristan Glatard; Jeremy Beaumont; Senan Doyle; Florence Forbes; Jesse Knight; April Khademi; Amirreza Mahbod; Chunliang Wang; Richard Mckinley; Franca Wagner; John Muschelli; Elizabeth Sweeney; Eloy Roura; Xavier Llado; Michel Santos; Wellington Santos; Abel Silva-Filho; Xavier Tomas-Fernandez; Hélène Urien; Isabelle Bloch; Sergi Valverde; Mariano Cabezas; Francisco Javier Vera-Olmos; Norberto Malpica; Charles Guttmann; Sandra Vukusic; Gilles Edan; Michel Dojat; Martin Styner; Simon Warfield; François Cotton; Christian Barillot

doi:10.1038/s41598-018-31911-7

Journal articles

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, 2} Sorina Camarasu Pop ⁴ Pascal Girard ⁴ Roxana Ameli ² Jean-Christophe Ferré ^{5, 1} Anne Kerbrat ^{1, 6} Thomas Tourdias ⁷ Frédéric Cervenansky ⁴ Tristan Glatard ⁸ Jeremy Beaumont ¹ Senan Doyle ⁹ Florence Forbes ^{10, 9} Jesse Knight ¹¹ April Khademi ¹² Amirreza Mahbod ¹³ Chunliang Wang ¹³ Richard Mckinley ¹⁴ Franca Wagner ¹⁴ John Muschelli ¹⁵ Elizabeth Sweeney ¹⁵ Eloy Roura ¹⁶ Xavier Llado ¹⁶ Michel Santos ¹⁷ Wellington Santos ¹⁷ Abel Silva-Filho ¹⁷ Xavier Tomas-Fernandez ¹⁸ Hélène Urien ¹⁹ Isabelle Bloch ¹⁹ Sergi Valverde ¹⁶ Mariano Cabezas ¹⁶ Francisco Javier Vera-Olmos ²⁰ Norberto Malpica ²⁰ Charles Guttmann ²¹ Sandra Vukusic ²² Gilles Edan ^{1, 4} Michel Dojat ²³ Martin Styner ²⁴ Simon Warfield ¹⁸ François Cotton ² Christian Barillot ¹

* Corresponding author

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

2 CHLS - Centre Hospitalier Lyon Sud [CHU - HCL]

3 LaTIM - Laboratoire de Traitement de l'Information Medicale

4 CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé

5 Département de Radiologie [CHU de Rennes]

6 CHU Pontchaillou [Rennes]

7 CHU Bordeaux [Bordeaux]

8 Concordia University [Montreal]

9 Pixyl Medical [Grenoble]

10 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems

Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann

11 University of Guelph

12 Ryerson University [Toronto]

13 KTH - Royal Institute of Technology [Stockholm]

14 University of Bern

15 Johns Hopkins Bloomberg School of Public Health [Baltimore]

16 VICOROB - Research institute of Computer Vision and Robotics [Girona]

17 UFPE - Universidade Federal de Pernambuco [Recife]

18 CRL - Computational Radiology Laboratory [Boston]

19 LTCI - Laboratoire Traitement et Communication de l'Information

20 URJC - Universidad Rey Juan Carlos [Madrid]

21 Brigham and Women's Hospital [Boston]

22 Service de Neurologie [Lyon]

23 GIN - [GIN] Grenoble Institut des Neurosciences

24 UNC - University of North Carolina [Chapel Hill]

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.

Document type :

Journal articles

Domain :

Life Sciences [q-bio] / Neurons and Cognition [q-bio.NC]

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https://www.hal.inserm.fr/inserm-01847873
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Submitted on : Tuesday, January 28, 2020 - 11:01:25 AM
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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

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