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

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arxiv 2107.01327 v1 pith:TOYOZTL7 submitted 2021-07-03 eess.IV cs.CV

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

Hoang C. Nguyen , Tung T. Le , Hieu H. Pham , Ha Q. Nguyen This is my paper
classification eess.IV cs.CV
keywords vindr-ribcxrindividualribssegmentationautomaticbenchmarkchestdataset
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

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  1. CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs

    cs.CV 2026-06 unverdicted novelty 5.0

    CheXanatomy trains VLMs to generate 2D anatomical masks via next-token prediction on synthetic CXRs from CT, matching U-Net performance with better domain-shift robustness and sample efficiency.

  2. RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection

    cs.CV 2026-05 unverdicted novelty 5.0

    RadGenome-Anatomy is a large-scale chest radiograph dataset with anatomy labels obtained by projecting 3D CT masks into 2D radiographic space for 210 structures in 25,692 studies.