# RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

arXiv: https://arxiv.org/abs/2109.09521 · HTML: https://mlsanity.com/paper/2109.09521/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [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)
- [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)
- [Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data](https://mlsanity.com/paper/1912.03681/) (2019)
- [IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning](https://mlsanity.com/paper/2003.02920/) (2020)
- [Deep Learning Based Rib Centerline Extraction and Labeling](https://mlsanity.com/paper/1809.07082/) (2018)
- [Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis](https://mlsanity.com/paper/1910.08878/) (2019)

## Built on by (most cited first, 21 of 21)

- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling](https://mlsanity.com/paper/2306.03934/) (2023)
- [Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines](https://mlsanity.com/paper/2307.13375/) (2023)
- [Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge](https://mlsanity.com/paper/2402.09372/) (2024)
- [LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging](https://mlsanity.com/paper/2502.20985/) (2025)
- [RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction](https://mlsanity.com/paper/2210.09309/) (2022)
- [PointScatter: Point Set Representation for Tubular Structure Extraction](https://mlsanity.com/paper/2209.05774/) (2022)
- [Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset](https://mlsanity.com/paper/2306.07089/) (2023)
- [Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fields](https://mlsanity.com/paper/2309.17329/) (2023)
- [Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis](https://mlsanity.com/paper/2206.15328/) (2022)
- [Representation-Agnostic Shape Fields](https://mlsanity.com/paper/2203.10259/) (2022)
- [Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding](https://mlsanity.com/paper/2212.02014/) (2022)
- [Improved distinct bone segmentation in upper-body CT through multi-resolution networks](https://mlsanity.com/paper/2301.13674/) (2023)
- [A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variation](https://mlsanity.com/paper/2508.03057/) (2025)
- [Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark](https://mlsanity.com/paper/2408.07444/) (2024)
- [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)
- [Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation](https://mlsanity.com/paper/2504.06741/) (2025)
- [Bonnet: Ultra-fast whole-body bone segmentation from CT scans](https://mlsanity.com/paper/2601.22576/) (2026)
- [SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images](https://mlsanity.com/paper/2607.22332/) (2026)

## Abstract (arXiv)

Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib segmentation methods can speed up the process through rib measurement and visualization. However, prior arts mostly use in-house labeled datasets that are publicly unavailable and work on dense 3D volumes that are computationally inefficient. To address these issues, we develop a labeled rib segmentation benchmark, named RibSeg, including 490 CT scans (11,719 individual ribs) from a public dataset. For ground truth generation, we used existing morphology-based algorithms and manually refined its results. Then, considering the sparsity of ribs in 3D volumes, we thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation. The proposed method achieves state-of-the-art segmentation performance (Dice~≈95%) with significant efficiency (10~40× faster than prior arts). The RibSeg dataset, code, and model in PyTorch are available at https://github.com/M3DV/RibSeg.