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Abstract #5198

Fully-Automated Segmentation of Knee Joint Anatomy using Deep Convolutional Neural Network Approach

Fang Liu1, Zhaoye Zhou2, and Richard Kijowski1

1Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 2Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States

A new fully-automated approach was proposed using a deep convolutional encoder-decoder (CED) network combined with 3D fully-connected conditional random field (CRF) and 3D simplex modeling for performing efficient and accurate multi-class musculoskeletal tissue segmentation from MR images. The deep learning-based segmentation method could be used to create 3D rendered models of all knee joint structures including cartilage, bone, tendon, meniscus, muscle, infrapatellar fat pad, and joint effusion and Baker’s cyst which may be sources of pain in patients with knee osteoarthritis. The results of our study serve as a first step to provide quantitative MR measures of musculoskeletal tissue degeneration in a highly time efficient manner which would be practical for use in large population-based osteoarthritis research studies.

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