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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

Hoang C. Nguyen , Tung T. Le , Hieu H. Pham , Ha Q. Nguyen This is my paper · ORCID
classification eess.IVcs.CV
keywords vindr-ribcxrindividualribssegmentationautomaticbenchmarkchestdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture

    cs.CV 2026-01 unverdicted novelty 6.0 of 10

    RadJEPA learns chest X-ray encoders from unlabeled images via latent prediction in a joint embedding architecture, exceeding prior state-of-the-art on classification, segmentation, and report generation.

  2. A Generative Foundation Model for Chest Radiography

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A diffusion-based generative model for chest X-rays, trained on 960k image-report pairs, improves downstream classification, segmentation, detection, and fairness when its outputs are used for data augmentation or pre...

  3. PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism

    eess.IV 2025-07 reject novelty 6.0 of 10

    Prompt2SegCXR is a lightweight dual-input model that segments six organs and seventeen diseases in chest X-rays from hand-drawn doodle prompts, alongside a new 23-class prompt dataset.

  4. Autonomous Computer Vision Development with Agentic AI

    cs.CV 2025-06 conditional novelty 5.0 of 10

    An LLM-based agent autonomously generated a SimpleMind configuration, trained, and ran inference for chest X-ray lungs, heart, and ribs segmentation, achieving mean dice scores of 0.96, 0.82, and 0.83.

  5. Chest X-ray Foundation Model with Global and Local Representations Integration

    eess.IV 2025-02 conditional novelty 4.0 of 10

    CheXFound, a ViT-Large model pretrained on 987K CXRs with DINOv2 plus the GLoRI head, outperforms prior CXR foundation models on long-tailed disease classification and transfer tasks.

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