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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2504.02382 |
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| _version_ | 1866917470051237888 |
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| author | Sang, Yudi Liu, Yanzhen Yibulayimu, Sutuke Wang, Yunning Killeen, Benjamin D. Liu, Mingxu Ku, Ping-Cheng Johannsen, Ole Gotkowski, Karol Zenk, Maximilian Maier-Hein, Klaus Isensee, Fabian Yue, Peiyan Wang, Yi Yu, Haidong Pan, Zhaohong He, Yutong Liang, Xiaokun Liu, Daiqi Fan, Fuxin Jurgas, Artur Skalski, Andrzej Ma, Yuxi Yang, Jing Płotka, Szymon Litka, Rafał Zhu, Gang Song, Yingchun Unberath, Mathias Armand, Mehran Ruan, Dan Zhou, S. Kevin Cao, Qiyong Zhao, Chunpeng Wu, Xinbao Wang, Yu |
| author_facet | Sang, Yudi Liu, Yanzhen Yibulayimu, Sutuke Wang, Yunning Killeen, Benjamin D. Liu, Mingxu Ku, Ping-Cheng Johannsen, Ole Gotkowski, Karol Zenk, Maximilian Maier-Hein, Klaus Isensee, Fabian Yue, Peiyan Wang, Yi Yu, Haidong Pan, Zhaohong He, Yutong Liang, Xiaokun Liu, Daiqi Fan, Fuxin Jurgas, Artur Skalski, Andrzej Ma, Yuxi Yang, Jing Płotka, Szymon Litka, Rafał Zhu, Gang Song, Yingchun Unberath, Mathias Armand, Mehran Ruan, Dan Zhou, S. Kevin Cao, Qiyong Zhao, Chunpeng Wu, Xinbao Wang, Yu |
| contents | The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant challenge due to complex anatomy and imaging limitations. The PENGWIN challenge, organized as a MICCAI 2024 satellite event, aimed to advance automated fracture segmentation by benchmarking state-of-the-art algorithms on these complex tasks. A diverse dataset of 150 CT scans was collected from multiple clinical centers, and a large set of simulated X-ray images was generated using the DeepDRR method. Final submissions from 16 teams worldwide were evaluated under a rigorous multi-metric testing scheme. The top-performing CT algorithm achieved an average fragment-wise intersection over union (IoU) of 0.930, demonstrating satisfactory accuracy. However, in the X-ray task, the best algorithm achieved an IoU of 0.774, which is promising but not yet sufficient for intra-operative decision-making, reflecting the inherent challenges of fragment overlap in projection imaging. Beyond the quantitative evaluation, the challenge revealed methodological diversity in algorithm design. Variations in instance representation, such as primary-secondary classification versus boundary-core separation, led to differing segmentation strategies. Despite promising results, the challenge also exposed inherent uncertainties in fragment definition, particularly in cases of incomplete fractures. These findings suggest that interactive segmentation approaches, integrating human decision-making with task-relevant information, may be essential for improving model reliability and clinical applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02382 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge Sang, Yudi Liu, Yanzhen Yibulayimu, Sutuke Wang, Yunning Killeen, Benjamin D. Liu, Mingxu Ku, Ping-Cheng Johannsen, Ole Gotkowski, Karol Zenk, Maximilian Maier-Hein, Klaus Isensee, Fabian Yue, Peiyan Wang, Yi Yu, Haidong Pan, Zhaohong He, Yutong Liang, Xiaokun Liu, Daiqi Fan, Fuxin Jurgas, Artur Skalski, Andrzej Ma, Yuxi Yang, Jing Płotka, Szymon Litka, Rafał Zhu, Gang Song, Yingchun Unberath, Mathias Armand, Mehran Ruan, Dan Zhou, S. Kevin Cao, Qiyong Zhao, Chunpeng Wu, Xinbao Wang, Yu Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant challenge due to complex anatomy and imaging limitations. The PENGWIN challenge, organized as a MICCAI 2024 satellite event, aimed to advance automated fracture segmentation by benchmarking state-of-the-art algorithms on these complex tasks. A diverse dataset of 150 CT scans was collected from multiple clinical centers, and a large set of simulated X-ray images was generated using the DeepDRR method. Final submissions from 16 teams worldwide were evaluated under a rigorous multi-metric testing scheme. The top-performing CT algorithm achieved an average fragment-wise intersection over union (IoU) of 0.930, demonstrating satisfactory accuracy. However, in the X-ray task, the best algorithm achieved an IoU of 0.774, which is promising but not yet sufficient for intra-operative decision-making, reflecting the inherent challenges of fragment overlap in projection imaging. Beyond the quantitative evaluation, the challenge revealed methodological diversity in algorithm design. Variations in instance representation, such as primary-secondary classification versus boundary-core separation, led to differing segmentation strategies. Despite promising results, the challenge also exposed inherent uncertainties in fragment definition, particularly in cases of incomplete fractures. These findings suggest that interactive segmentation approaches, integrating human decision-making with task-relevant information, may be essential for improving model reliability and clinical applicability. |
| title | Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.02382 |