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Abstract #1404

Assessing the performance of knee meniscus segmentation with deep convolutional neural networks in 3D ultrashort echo time (UTE) Cones MR imaging

Michal Byra1,2, Mei Wu1, Xiaodong Zhang1, Hyungseok Jang1, Yajun Ma1, Eric Chang1,2, Sameer Shah3, and Jiang Du1

1Department of Radiology, University of California, San Diego, CA, United States, 2Radiology Service, VA San Diego Healthcare System, San Diego, CA, United States, 3Departments of Orthopedic Surgery and Bioengineering, University of California, San Diego, CA, United States

Automatic segmentation of the knee menisci would facilitate quantitative and morphological evaluation in diseases such as osteoarthritis. We propose a deep convolutional neural network for the segmentation of 3D UTE-Cones Adiabatic T1ρ-weighted volumes of the meniscus. To show the usefulness of the proposed method, we developed the models using regions of interests provided by two radiologists. The method produced strong Dice scores and consistent results with respect to meniscus volume measurement. The inter-observer agreement between the models and the radiologists was very similar to that of the radiologists alone.

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