📌 News¶

✅ [April 7] The training datasets are now available for download.

✅ [May 14] Submission for the preliminary development phase is now open. Please check the guidelines to prepare for your contrainer submission.

✅ [July 10] Submission for the final test phase is now open. Deadline: August 16 (the exact local time can be found on the submission page).

✅ [August 17] To accommodate ongoing submissions, the organizers have extended the deadline by 72 hours. Participants who have already made their final submission will be allowed to update their submission.

✅ [August 30] Winners for the final test phase have been annouced. Congrats!

🐧 Background¶

Pelvic fractures, typically resulting from high-energy traumas, are among the most severe injuries, characterized by a disability rate over 50% and a mortality rate over 13%, ranking them as the deadliest of all compound fractures. The complexity of pelvic anatomy, along with surrounding soft tissues, makes surgical interventions especially challenging. Recent years have seen a shift towards the use of robotic-assisted closed fracture reduction surgeries, which have shown improved surgical outcomes. Accurate segmentation of pelvic fractures is essential, serving as a critical step in trauma diagnosis and image-guided surgery. In 3D CT scans, fracture segmentation is crucial for fracture typing, pre-operative planning for fracture reduction, and screw fixation planning. For 2D X-ray images, segmentation plays a vital role in transferring the surgical plan to the operating room via registration, a key step for precise surgical navigation. 

📋 Tasks¶

¶

As a MICCAI 2024 challenge, the PENGWIN segmentation challenge is designed to advance the development of automated pelvic fracture segmentation techniques in both 3D CT scans (Task 1) and 2D X-ray images (Task 2), aiming to enhance their accuarcy and robustness. Our dataset comprises CT scans from 150 patients scheduled for pelvic reduction surgery, collected from multiple institutions using a variety of scanning equipment. This dataset represents a diverse range of patient cohorts and fracture types. Ground-truth segmentations for sacrum and hipbone fragments have been semi-automatically annotated and subsequently validated by medical experts. Furthermore, we have generated high-quality, realistic X-ray images and corresponding 2D labels from the CT data using the DeepDRR method, incorporating a range of virtual C-arm camera positions and surgical tools. 

TL;DR
Task 1: Pelvic fragment segmentation on 3D CT
Task 2: Pelvic fragment segmentation on 2D X-ray

📎 Citation¶

@article{LIUandYIBULAYIMU2025MEDIA,
title = {Preoperative fracture reduction planning for image-guided pelvic trauma surgery: A comprehensive pipeline with learning},
journal = {Medical Image Analysis},
pages = {103506},
year = {2025},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2025.103506},
url = {https://www.sciencedirect.com/science/article/pii/S1361841525000544},
author = {Yanzhen Liu and Sutuke Yibulayimu and Yudi Sang and Gang Zhu and Chao Shi and Chendi Liang and Qiyong Cao and Chunpeng Zhao and Xinbao Wu and Yu Wang},
}

@inproceedings{liu2023pelvic,
  title={Pelvic Fracture Segmentation Using a Multi-scale Distance-Weighted Neural Network},
  author={Liu, Yanzhen and Yibulayimu, Sutuke and Sang, Yudi and Zhu, Gang and Wang, Yu and Zhao, Chunpeng and Wu, Xinbao},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={312--321},
  year={2023},
  organization={Springer}
}