Bone Segmentation

Bone segmentation, the automated identification of bone structures in medical images, aims to improve the accuracy and efficiency of diagnosis and treatment planning in various musculoskeletal applications. Current research focuses on developing robust deep learning models, including U-Net variants, GANs, and transformer networks, often incorporating techniques like multi-resolution processing, cross-scale attention, and semi-supervised learning to address challenges posed by image quality, anatomical variability, and limited annotated data. These advancements are crucial for improving the accuracy of fracture detection, surgical planning, and the quantitative assessment of bone diseases, ultimately leading to better patient care. The development of universal models capable of segmenting any bone in various imaging modalities (CT, MRI, ultrasound) represents a significant ongoing effort.