Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the lack of large-scale annotated datasets and evaluation benchmarks has hindered the development and validation of deep learning algorithms. To address this issue, the RibFrac Challenge was introduced, providing a benchmark dataset of over 5,000 rib fractures from 660 CT scans, with voxel-level instance mask annotations and diagnosis labels for four clinical categories (buckle, nondisplaced, displaced, or segmental). The challenge includes two tracks: a detection (instance segmentation) track evaluated by an FROC-style metric and a classification track evaluated by an F1-style metric. During the MICCAI 2020 challenge period, 243 results were evaluated, and seven teams were invited to participate in the challenge summary. The analysis revealed that several top rib fracture detection solutions achieved performance comparable or even better than human experts. Nevertheless, the current rib fracture classification solutions are hardly clinically applicable, which can be an interesting area in the future. As an active benchmark and research resource, the data and online evaluation of the RibFrac Challenge are available at the challenge website. As an independent contribution, we have also extended our previous internal baseline by incorporating recent advancements in large-scale pretrained networks and point-based rib segmentation techniques. The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.

翻译：肋骨骨折是一种常见且可能严重的损伤，在CT扫描中检测时既具挑战性又耗费人力。尽管该领域已有相关研究，但大规模标注数据集和评估基准的缺乏阻碍了深度学习算法的开发与验证。为解决此问题，RibFrac挑战赛应运而生，提供了包含660例CT扫描中5000多处肋骨骨折的基准数据集，附带体素级实例掩膜标注及四种临床类别（青枝型、非移位型、移位型或节段型）的诊断标签。该挑战设两个赛道：检测赛道（实例分割）采用类FROC指标评估，分类赛道采用类F1指标评估。在MICCAI 2020挑战赛期间，共评估了243项成果，其中七个团队受邀参与挑战总结。分析表明，多个顶尖肋骨骨折检测方案的性能已达到甚至超越人类专家水平。然而，当前肋骨骨折分类方案尚难以直接应用于临床，这将成为未来值得探索的方向。作为活跃的基准与研究资源，RibFrac挑战赛的数据及在线评估入口均可在官网获取。作为独立贡献，我们还通过整合大规模预训练网络和基于点的肋骨分割技术的最新进展，对先前内部基准进行了扩展。由此产生的FracNet+在肋骨骨折检测中展现出具有竞争力的性能，为人工智能辅助肋骨骨折检测与诊断的进一步研究开发奠定了基础。