Vertebra Segmentation

Vertebra segmentation, the automated identification and delineation of individual vertebrae in medical images (CT, MRI, X-ray, ultrasound), aims to improve the accuracy and efficiency of spinal diagnoses and treatment planning. Current research emphasizes developing robust and accurate segmentation models, often employing deep learning architectures like U-Net and its variants, incorporating techniques such as contrastive learning, graph convolutional networks, and attention mechanisms to address challenges posed by image variability and anatomical complexity. These advancements hold significant promise for improving the speed and precision of tasks ranging from fracture detection and scoliosis assessment to surgical planning and tumor localization, ultimately leading to better patient care.