Deep Rib Fracture Instance Segmentation and Classification from CT on
  the RibFrac Challenge
Jiancheng Yang Rui Shi Liang Jin Xiaoyang Huang Kaiming Kuang Donglai Wei Shixuan Gu Jianying Liu Pengfei Liu Zhizhong Chai Yongjie Xiao Hao Chen Liming Xu Bang Du Xiangyi Yan Hao Tang Adam Alessio Gregory Holste Jiapeng Zhang Xiaoming Wang Jianye He Lixuan Che Hanspeter Pfister Ming Li Bingbing Ni
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.