We describe a network for automatic segmentation of acetabulum and femur on 3D-Dixon MRI data. Given the limited number of labeled 3D hip datasets publicly available, our network was trained using transfer learning from a network previously developed for the segmentation of the shoulder bony structures. Using only 5 hip datasets for training, our network achieved segmentation dice of 0.719 and 0.92 for acetabulum and femur, respectively. More training data is needed to improve results for the acetabulum. We show that transfer learning can enable automatic segmentation of the hip bones using a limited number of labeled training data.
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