Spine Segmentation

Spine segmentation, the automated identification of spinal structures in medical images (like MRI and CT scans), aims to improve the speed and accuracy of diagnosis and treatment planning for spinal diseases. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and transformer-based models, often incorporating techniques like shape priors and multi-task learning to enhance segmentation accuracy and address challenges posed by anatomical complexity and image noise. These advancements enable more precise identification of vertebrae, intervertebral discs, and other spinal structures, facilitating improved assessment of conditions such as scoliosis and spinal stenosis. The development of robust and publicly available algorithms, along with large annotated datasets, is driving progress and fostering wider collaboration within the field.