  Automatic rib labeling and anatomical centerline extraction are common
prerequisites for various clinical applications. Prior studies either use
in-house datasets that are inaccessible to communities, or focus on rib
segmentation that neglects the clinical significance of rib labeling. To
address these issues, we extend our prior dataset (RibSeg) on the binary rib
segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT
scans (15,466 individual ribs in total) and annotations manually inspected by
experts for rib labeling and anatomical centerline extraction. Based on the
RibSeg v2, we develop a pipeline including deep learning-based methods for rib
labeling, and a skeletonization-based method for centerline extraction. To
improve computational efficiency, we propose a sparse point cloud
representation of CT scans and compare it with standard dense voxel grids.
Moreover, we design and analyze evaluation metrics to address the key
challenges of each task. Our dataset, code, and model are available online to
facilitate open research at https://github.com/M3DV/RibSeg
