Hierarchical Loss And Geometric Mask Refinement For Multilabel Ribs Segmentation
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Abstract (arXiv)
Automatic ribs segmentation and numeration can increase computed tomography assessment speed and reduce radiologists mistakes. We introduce a model for multilabel ribs segmentation with hierarchical loss function, which enable to improve multilabel segmentation quality. Also we propose postprocessing technique to further increase labeling quality. Our model achieved new state-of-the-art 98.2% label accuracy on public RibSeg v2 dataset, surpassing previous result by 6.7%.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 24 May 2024.
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References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
- 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
- 2018nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentationcited by 291
- 2022RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extractioncited by 11
- 2018Deep Learning Based Rib Centerline Extraction and Labelingcited by 10
- 2022Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localizationcited by 8
- 2019Down to the Last Detail: Virtual Try-on with Detail Carvingcited by 4
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