CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

Spine-related diseases have high morbidity and cause a huge burden of social cost. Spine imaging is an essential tool for noninvasively visualizing and assessing spinal pathology. Segmenting vertebrae in computed tomography (CT) images has always been the base of quantitative medical image analysis for clinical diagnosis and surgery planning of spine diseases. Current publicly available annotated datasets on spinal vertebrae are small in size. Due to the lack of a large-scale annotated spine image dataset, the mainstream deep learning-based segmentation methods, which are data-driven, are heavily restricted. In this paper, we introduce a large-scale spine CT dataset called CTSpine1K, curated from multiple sources for vertebra segmentation, which contains 1,005 CT volumes with over 500,000 labeled vertebrae slices and 11,172 vertebrae belonging to different spinal conditions. Based on this dataset, we conducted several spinal vertebrae segmentation experiments to set the first benchmark. We believe that this large-scale dataset will facilitate further research in many spine-related image analysis tasks, including but not limited to vertebrae segmentation, labeling, 3D spine reconstruction from biplanar radiographs, and image superresolution and enhancement. Our dataset are publically available at https://xnat.health-ri.nl/data/archive/projects/africai_miccai2024_ctspine1k and https://github.com/MIRACLE-Center/CTSpine1K.