Abstract #4743

A neural network to estimate the hip center of rotation for a fully-automated range of motion analysis in femoroacetabular impingement.

Eros Montin^1,2, Daniele Panozzo³, and Riccardo Lattanzi^1,2,4

¹Center for Advanced Imaging Innovation and Research (CAI2R) Department of Radiology, Radiology Department, New York University Grossman School of Medicine, New York, New York, USA, New York, NY, United States, ²Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA, New York, NY, United States, ³New York University, New York, New York, USA, BROOKLYN, NY, United States, ⁴Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA, New York, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, JointsWe evaluated three neural network architectures for the automatic identification of the center of the femur head on 3D water-only Dixon MRI. We trained using a mixture of real and augmented data. The mean error of the best-performing network was three-time lower compared to a manual annotation and on the order of 1 voxel. We combined the network to create the first fully automated pipeline to assess the hip range of motion from 3D MR.

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