# RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

arXiv: https://arxiv.org/abs/2210.09309 · HTML: https://mlsanity.com/paper/2210.09309/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 79 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts](https://mlsanity.com/paper/1406.2031/) (2014)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling](https://mlsanity.com/paper/1904.03375/) (2019)
- [Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion](https://mlsanity.com/paper/2111.12702/) (2021)
- [Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy](https://mlsanity.com/paper/1809.04430/) (2018)
- [VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes](https://mlsanity.com/paper/1809.00226/) (2018)
- [3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis](https://mlsanity.com/paper/2112.04863/) (2021)
- [RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans](https://mlsanity.com/paper/2109.09521/) (2021)
- [A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification](https://mlsanity.com/paper/2009.03671/) (2020)
- [Deep Learning Based Rib Centerline Extraction and Labeling](https://mlsanity.com/paper/1809.07082/) (2018)
- [DeepFlux for Skeletons in the Wild](https://mlsanity.com/paper/1811.12608/) (2018)
- [A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms](https://mlsanity.com/paper/2006.16161/) (2020)
- [Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans](https://mlsanity.com/paper/2005.03654/) (2020)

## Built on by (most cited first, 11 of 11)

- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge](https://mlsanity.com/paper/2402.09372/) (2024)
- [Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fields](https://mlsanity.com/paper/2309.17329/) (2023)
- [Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark](https://mlsanity.com/paper/2408.07444/) (2024)
- [Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort](https://mlsanity.com/paper/2505.05004/) (2025)
- [RibPull: Implicit Occupancy Fields and Medial Axis Extraction for CT Ribcage Scans](https://mlsanity.com/paper/2509.01402/) (2025)
- [Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear Structures](https://mlsanity.com/paper/2404.14435/) (2024)
- [Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation](https://mlsanity.com/paper/2405.15500/) (2024)
- [Bonnet: Ultra-fast whole-body bone segmentation from CT scans](https://mlsanity.com/paper/2601.22576/) (2026)
- [CTSpinoPelvic1K: spine, pelvis, ribs and femora in one coordinate frame, annotated for lumbosacral transitional anatomy](https://mlsanity.com/paper/2609.22760/) (2026)
- [Hybrid Context Forests and Bilateral Viterbi Matching for 3D Rib Centerline Tracing in CT](https://mlsanity.com/paper/2610.16047/) (2026)

## Abstract (arXiv)

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