bioRxiv / medRxiv
open access
3D Segmentation of Pathological Muscle with a Physics-Informed Latent-Regularized U-Net
ObjectiveWe developed a data-efficient deep learning framework for three-dimensional segmentation of pathological musculoskeletal anatomy from magnetic resonance imaging (MRI) when only limited manual annotations are available. MethodsWe developed a Physics-Informed Latent-Regularized U-Net (PILR-U
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