mlsanityData to 7 Oct 2026

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

arXiv 2109.09521 ↗First posted 17 Sep 2021eess.IV cs.AI cs.CVcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 17 Sep 2021.

Builds on

13 of 26 references.

References · 13

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  4. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  5. 2017Deep Setscited by 1,394
  6. 2021The Medical Segmentation Decathloncited by 620
  7. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  8. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  9. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  10. 2019Voxel2Mesh: 3D Mesh Model Generation from Volumetric Datacited by 52
  11. 2020IntrA: 3D Intracranial Aneurysm Dataset for Deep Learningcited by 41
  12. 2018Deep Learning Based Rib Centerline Extraction and Labelingcited by 9
1 more
  1. 2019Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysiscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 5; 2024: 3; 2025: 5; 2026: 2.

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  1. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 39
  2. 2022Detailed Annotations of Chest X-Rays via CT Projection for Report Understandingcited by 24
  3. 2023Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labelingcited by 24
  4. 2023Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelinescited by 17
  5. 2024Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challengecited by 14
  6. 2025LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imagingcited by 11
  7. 2022RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extractioncited by 10
  8. 2022PointScatter: Point Set Representation for Tubular Structure Extractioncited by 8
  9. 2023Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Datasetcited by 6
  10. 2023Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fieldscited by 6
  11. 2022Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysiscited by 5
  12. 2022Representation-Agnostic Shape Fieldscited by 4
9 more
  1. 2022Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embeddingcited by 3
  2. 2023Improved distinct bone segmentation in upper-body CT through multi-resolution networkscited by 2
  3. 2025A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variationcited by 2
  4. 2024Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmarkcited by 1
  5. 2025RibPull: Implicit Occupancy Fields and Medial Axis Extraction for CT Ribcage Scanscited by 1
  6. 2024Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear Structurescited by 0
  7. 2025Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentationcited by 0
  8. 2026Bonnet: Ultra-fast whole-body bone segmentation from CT scanscited by 0
  9. 2026SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Imagescited by 0