Spinal Cord

Spinal cord research is intensely focused on developing accurate and efficient methods for segmenting and analyzing spinal cord structures from magnetic resonance imaging (MRI) data. Current efforts leverage deep learning architectures, such as U-Net and Transformer-based models, to automate the segmentation of various features, including the spinal cord itself, lesions, nerve rootlets, and tissue bridges. These advancements are crucial for improving the diagnosis and monitoring of spinal cord injuries and diseases like multiple sclerosis, enabling more objective and quantitative assessments of disease progression and treatment efficacy. The development of open-source tools and large, diverse datasets is accelerating progress in this field.