Upload README_0013_ribfrac.md

0013_ribfrac/README_0013_ribfrac.md

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+# RibFrac Challenge Dataset
+
+## License
+**CC BY-NC 4.0**  
+[Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{ribfracchallenge2025,
+    title={Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge},
+    author={Yang, Jiancheng and Shi, Rui and Jin, Liang and Huang, Xiaoyang and Kuang, Kaiming and Wei, Donglai and Gu, Shixuan and Liu, Jianying and Liu, Pengfei and Chai, Zhizhong and Xiao, Yongjie and Chen, Hao and Xu, Liming and Du, Bang and Yan, Xiangyi and Tang, Hao and Alessio, Adam and Holste, Gregory and Zhang, Jiapeng and Wang, Xiaoming and He, Jianye and Che, Lixuan and Pfister, Hanspeter and Li, Ming and Ni, Bingbing},
+    journal={IEEE Transactions on Medical Imaging},
+    year={2025}
+}
+```
+
+Paper BibTeX:
+
+```bibtex
+@article{ribfracclinical2020,
+      title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},
+      author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},
+      journal={eBioMedicine},
+      year={2020},
+      publisher={Elsevier}
+  }
+```
+
+
+## Dataset description
+The RibFrac Challenge provides a large-scale benchmark for automated rib fracture detection and classification. It contains around 5,000 annotated rib fractures from 660 CT scans, with pixel-level fracture masks and four-class fracture type labels, supporting both detection and classification research.
+
+**Challenge homepage**: https://ribfrac.grand-challenge.org/overview/
+
+**Number of CT volumes**: 660
+
+**Contrast**: Multi-detector CT scanners (16 cm wide coverage and dual-source CT)
+
+**CT body coverage**: Chest-abdomen
+
+**Does the dataset include any ground truth annotations?**: Only labels for classification
+
+**Original GT annotation targets**: Only rib fracture regions and types
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: Human experts with deep learning assistance (human-in-the-loop)
+
+**Acquisition centers**: Huadong Hospital, affiliated with Fudan University in Shanghai, China
+
+**Pathology/Disease**: Traumatic rib fractures, with various fracture types
+
+**Original dataset download link**: https://ribfrac.grand-challenge.org/dataset/
+
+**Original dataset format**: nifti