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Abstract #2661

Automated Cartilage Segmentation for Clinical Knee MR Images using Transfer Learning

Mingrui Yang1, Ceylan Colak2, Andreas Nanavati1, Sibaji Gaj1, Carl Winalski2, Naveen Subhas2, and Xiaojuan Li1
1Program of Advanced Musculoskeletal Imaging, Cleveland Clinic, Cleveland, OH, United States, 2Radiology, Cleveland Clinic, Cleveland, OH, United States

Laborious and time-consuming manual or semi-automatic cartilage and meniscus segmentation, which in addition suffers from intra and inter reader variability, has been one of the major hurdles of developing and applying techniques such as quantitative magnetic resonance imaging in routine clinical practice for improved osteoarthritis patient treatment and management plans. In addition, effective and robust deep learning based automatic cartilage and meniscus segmentation models are still lacking in heterogenous clinical settings. The purpose of this study is to assess the feasibility of building an automatic cartilage segmentation model using transfer learning with limited and heterogenous clinical MR scans.

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