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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
Hosp Civils Lyon, Lyon Sud Hosp, Dept Radiol, Lyon, France..
Univ Rennes 1, CNRS, INSERM, VISAGES,U1228,UMR6074, Rennes, France..
Univ Brest, IBSAM, INSERM, LaTIM,UMR 1101, Brest, France..
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2018 (Engelska)Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, artikel-id 13650Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Nature Publishing Group, 2018. Vol. 8, artikel-id 13650
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URN: urn:nbn:se:kth:diva-235440DOI: 10.1038/s41598-018-31911-7ISI: 000444374900001PubMedID: 30209345Scopus ID: 2-s2.0-85053246791OAI: oai:DiVA.org:kth-235440DiVA, id: diva2:1251453
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QC 20180927

Tillgänglig från: 2018-09-27 Skapad: 2018-09-27 Senast uppdaterad: 2018-10-09Bibliografiskt granskad

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Mahbod, AmirrezaWang, Chunliang

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