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

Commowick, Olivier; Istace, Audrey; Kain, Michaël; Laurent, Baptiste; Leray, Florent; Simon, Mathieu; Pop, Sorina Camarasu; Girard, Pascal; Améli, Roxana; Ferré, Jean-Christophe; Kerbrat, Anne; Tourdias, Thomas; Cervenansky, Frédéric; Glatard, Tristan; Beaumont, Jérémy; Doyle, Senan; Forbes, Florence; Knight, Jesse; Khademi, April; Mahbod, Amirreza; ... (2018). Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. Scientific Reports, 8(1), p. 13650. Nature Publishing Group 10.1038/s41598-018-31911-7

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

McKinley, Richard

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2045-2322

Publisher:

Nature Publishing Group

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

24 Sep 2018 11:20

Last Modified:

30 Sep 2018 02:30

Publisher DOI:

10.1038/s41598-018-31911-7

PubMed ID:

30209345

BORIS DOI:

10.7892/boris.120088

URI:

https://boris.unibe.ch/id/eprint/120088

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