Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
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Olivier Commowick
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- Function : Correspondent author
- PersonId : 14904
- IdHAL : olivier-commowick
- ORCID : 0000-0001-7345-6752
- IdRef : 113377983
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Sorina Camarasu Pop
- Function : Author
- PersonId : 19719
- IdHAL : camarasu
- ORCID : 0000-0002-7923-5069
- IdRef : 178745596
Pascal Girard
- Function : Author
- PersonId : 829460
Jean-Christophe Ferré
- Function : Author
- PersonId : 14483
- IdHAL : jean-christopheferre
- ORCID : 0000-0003-3911-1800
- IdRef : 086442937
Frédéric Cervenansky
- Function : Author
- PersonId : 19446
- IdHAL : frederic-cervenansky
Tristan Glatard
- Function : Author
- PersonId : 758474
- ORCID : 0000-0003-2620-5883
Florence Forbes
- Function : Author
- PersonId : 16305
- IdHAL : florence-forbes
- ORCID : 0000-0003-3639-0226
- IdRef : 12469781X
Isabelle Bloch
- Function : Author
- PersonId : 175825
- IdHAL : isabelle-bloch
- ORCID : 0000-0002-6984-1532
- IdRef : 031277861
Sandra Vukusic
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- PersonId : 740274
- IdHAL : adil-maarouf
- ORCID : 0000-0002-6755-496X
- IdRef : 164833250
Michel Dojat
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- PersonId : 1720
- IdHAL : michel-dojat
- ORCID : 0000-0003-2747-6845
- IdRef : 075440202
François Cotton
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- PersonId : 170687
- IdHAL : francois-cotton
- ORCID : 0000-0003-0046-2478
- IdRef : 070732035
Christian Barillot
- Function : Author
- PersonId : 1245
- IdHAL : cbarillot
- ORCID : 0000-0002-1589-7696
- IdRef : 074123637
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.
Origin : Publication funded by an institution
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