Details

Autor(en) / Beteiligte

• 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
• Wang, Chunliang
• McKinley, Richard
• Wagner, Franca
• Muschelli, John
• Sweeney, Elizabeth
• Roura, Eloy
• Lladó, Xavier
• Santos, Michel M
• Santos, Wellington P
• Silva-Filho, Abel G
• Tomas-Fernandez, Xavier
• Urien, Hélène
• Bloch, Isabelle
• Valverde, Sergi
• Cabezas, Mariano
• Vera-Olmos, Francisco Javier
• Malpica, Norberto
• Guttmann, Charles
• Vukusic, Sandra
• Edan, Gilles
• Dojat, Michel
• Styner, Martin
• Warfield, Simon K
• Cotton, François
• Barillot, Christian

Titel

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

Ist Teil von

• Scientific reports, 2018-09, Vol.8 (1), p.13650-17, Article 13650

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2018

Link zum Volltext

Quelle

EZB*

Beschreibungen/Notizen

• 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.