Abstract #1412
Automatic segmentation of the hip bony structures on 3D Dixon MRI datasets using transfer learning from a neural network developed for the shoulder
Eros Montin1, Cem Murat Deniz1, Tatiane Cantarelli Rodrigues2, Soterios Gyftopoulos3, Richard Kijowski3, and Riccardo Lattanzi1,4
1Center for Advanced Imaging Innovation and Research (CAI2R) Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, São Paulo, Brazil, 3Department of radiology, New York University Grossman School of Medicine, New York, NY, United States, 4Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine,, New York, NY, United States
Synopsis
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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