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Sci Rep ; 8(1): 13650, 2018 09 12.
Artigo em Inglês | MEDLINE | ID: mdl-30209345

RESUMO

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.


Assuntos
Algoritmos , Imageamento por Ressonância Magnética/métodos , Esclerose Múltipla/diagnóstico por imagem , Esclerose Múltipla/diagnóstico , Tecido Parenquimatoso/diagnóstico por imagem , Feminino , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Masculino , Esclerose Múltipla/patologia , Redes Neurais de Computação , Tecido Parenquimatoso/patologia , Estudos Retrospectivos
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