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Abstract #3561

Whole Knee Cartilage Segmentation using Deep Convolutional Neural Networks for Quantitative 3D UTE Cones Magnetization Transfer  Modeling

Yanping Xue1,2, Hyungseok Jang1, Zhenyu Cai1, Hoda Shirazian1, Mei Wu1, Michal Byra1, Yajun Ma1, Eric Y Chang1,3, and Jiang Du1
1University of California, San Diego, San Diego, CA, United States, 2Beijing Chao-Yang Hospital, Beijing, China, 3VA San Diego Healthcare System, San Diego, CA, United States

The existence of short T2 tissues and high ordered collagen fibers in cartilage render it “invisible” to conventional MR and sensitive to the magic angle effect. Segmentation is the first step to obtain parameters of cartilage, which is often performed manually (time-consuming and variable). Automatic segmentation and providing a biomarker that visualizes both short and long T2 tissues and insensitive to the magic angle effect is desideratum. U-Net is based on CNN to process images. The purpose of this study is to describe and evaluate the pipeline of fully-automatic segmentation of cartilage and extraction of MMF in 3D UTE-Cones-MT modeling.

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