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Abstract #4308

Fully Automatic Segmentation of Knee Joint Anatomy and Lesions using Clinical MR Images

Andrew Seohwan Yu1,2, Mingrui Yang1, Sercan Tosun1, Richard Lartey1, Kunio Nakamura1, Naveen Subhas1, Cark Winalski1, and Xiaojuan Li1
1Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States, 2Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, United States

Synopsis

Keywords: Segmentation, Segmentation, Data Processing, MSK, Osteoarthritis, Joints

Motivation: Osteoarthritis affects multiple tissues in the knee joint. However, there is a lack of an efficient method for automatic segmentation of tissues and lesions using a single clinical MRI sequence.

Goal(s): To provide a solution for automatic segmentation of femur and tibia bone and cartilage, plus bone marrow edema-like lesions (BMEL) using IW-TSE images only.

Approach: We trained a multi-label segmentation model in a supervised manner, employing pre- and post-processing steps to improve its robustness and stability.

Results: We find that a lightweight convolutional neural network can be trained to segment the five regions with a combined Dice similarity coefficient (DSC) of 0.87.

Impact: We provide an efficient and consistent solution for the segmentation of knee joint anatomy and lesions, enabling large-scale downstream analyses without incurring large costs for manual annotations.

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