  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 is the basis 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 11,100 labeled vertebrae belonging to different spinal
conditions. Based on this dataset, we conduct 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, image
super-resolution, and enhancement.
