📌 News
✅ The training datasets are now available for download.
🐧 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
Subject to change.
@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}
}