Musculoskeletal Segmentation

Musculoskeletal segmentation focuses on automatically identifying and delineating different anatomical structures (bones, muscles, etc.) within medical images like X-rays, CT scans, and MRIs. Current research emphasizes improving the accuracy and efficiency of deep learning models, particularly U-Net architectures, often incorporating techniques like Bayesian active learning to reduce the need for extensive manual annotation and uncertainty estimation to flag unreliable segmentations. These advancements are crucial for improving diagnostic accuracy in various musculoskeletal conditions, enabling more precise quantitative analysis of muscle volume and density, and facilitating the development of advanced human-computer interfaces.