Peer-Reviewed Publication
Diagn Interv Imaging2019;100(4):243-249.April 1, 2019Journal Article
Artificial intelligence to diagnose meniscus tears on MRI.
V Roblot1, Y Giret2, M Bou Antoun3, C Morillot4, X Chassin4, A Cotten5, J Zerbib3, L Fournier6
1UMR-S970, Department of Radiology, Hôpital Européen Georges-Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris-Descartes, 75015 Paris, France. Electronic address: victoire.roblot@aphp.fr.
2CentraleSupélec, Université Paris Saclay, 91190 Gif-sur-Yvette, France; Foodvisor, 75011 Paris, France.
3UMR-S970, Department of Radiology, Hôpital Européen Georges-Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris-Descartes, 75015 Paris, France.
4CentraleSupélec, Université Paris Saclay, 91190 Gif-sur-Yvette, France.
5Department of Musculoskeletal Radiology, Lille University Hospital, 59037 Lille, France.
6UMR-S970, Department of Radiology, Hôpital Européen Georges-Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris-Descartes, 75015 Paris, France; Laboratoire de Recherche en Imagerie, LRI, PARCC-HEGP, UMR 970, Inserm/université Paris Descartes, Sorbonne-Paris cité, 75015 Paris, France.
Abstract
PURPOSE: The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.
MATERIAL AND METHODS: An algorithm was trained on a dataset of 1123 MR images of the knee. We separated the main task into three sub-tasks: first to detect the position of both horns, secon…
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