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Abstract #4062

Identification of Bone Marrow Lesions on Magnetic Resonance Imaging with Weakly Supervised Deep Learning

Jiaping Hu1, Zhao Wang2, Lijie Zhong1, Keyan Yu1, Yanjun Chen1, Yingjie Mei3, Qi Dou4, and Xiaodong Zhang1
1Department of Medical Imaging, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China, 2College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China, 3China International Center, Philips Healthcare, Guangzhou, China, 4Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, China

The presence of a bone marrow lesion is associated with incident and progressive knee osteoarthritis (KOA) and joint replacement. Since the ill-defined boundary and various signal strength, identification of bone marrow lesions (BMLs) requires professional diagnostic ability and is subjective. Therefore, we utilize a model to assess whether there exists BMLs in every subregion and their severity on 3D-dual echo steady state (DESS) images according to MRI Osteoarthritis Knee Score (MOAKS). The initiatory results showed that deep learning framework performed well on discrimination of BMLs with good reproducibility.

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