RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
This work provides a comprehensive benchmark for clinical applications in medical imaging, though it is incremental as it builds upon prior datasets and methods.
The authors tackled the lack of accessible benchmarks for rib labeling and anatomical centerline extraction in medical imaging by extending their prior dataset to RibSeg v2, which includes 660 CT scans with 15,466 ribs and expert annotations, and developed a pipeline with deep learning methods and evaluation metrics, achieving computational efficiency improvements through sparse point cloud representations.
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