mlsanityData to 7 Oct 2026

VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays

arXiv 2107.01327 ↗First posted 3 Jul 2021eess.IV cs.CVcited by 22

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Abstract (arXiv)

We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations provided by human experts. A set of state-of-the-art segmentation models are trained on 196 images from the VinDr-RibCXR to segment and label 20 individual ribs. Our best performing model obtains a Dice score of 0.834 (95% CI, 0.810--0.853) on an independent test set of 49 images. Our study, therefore, serves as a proof of concept and baseline performance for future research.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 6 of its 7 references that are in the map. First posted on arXiv 3 Jul 2021.

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6 of 7 references.

References · 6

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  4. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  5. 2020VinDr-CXR: An open dataset of chest X-rays with radiologist's annotationscited by 236
  6. 2019Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNNcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 4; 2025: 6; 2026: 5.

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  1. 2024Exploring scalable medical image encoders beyond text supervisioncited by 129
  2. 2024Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learningcited by 18
  3. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  4. 2022Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysiscited by 13
  5. 2024Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervisioncited by 12
  6. 2023Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervisioncited by 11
  7. 2023Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performancecited by 11
  8. 2022An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiographcited by 7
  9. 2022Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamesecited by 5
  10. 2025Chest X-ray Foundation Model with Global and Local Representations Integrationcited by 5
  11. 2025Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysiscited by 3
  12. 2023Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methodscited by 2
10 more
  1. 2023An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosiscited by 2
  2. 2026A data- and compute-efficient chest X-ray foundation model beyond aggressive scalingcited by 2
  3. 2025Autonomous Computer Vision Development with Agentic AIcited by 1
  4. 2025Semantic Alignment of Unimodal Medical Text and Vision Representationscited by 0
  5. 2025PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanismcited by 0
  6. 2025Multi Anatomy X-Ray Foundation Modelcited by 0
  7. 2026RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecturecited by 0
  8. 2026ViX-Ray: A Vietnamese Chest X-Ray Dataset for Vision-Language Modelscited by 0
  9. 2026CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographscited by 0
  10. 2026FleXray: Universal Clinical X-ray Segmentationcited by 0