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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