Pelvic fracture is a severe type of high-energy injury. Segmentation of pelvic fractures from 3D CT images is important for trauma diagnosis, evaluation, and treatment planning. Manual delineation of the fracture surface can be done in a slice-by-slice fashion but is slow and error-prone. Automatic fracture segmentation is challenged by the complex structure of pelvic bones and the large variations in fracture types and shapes.
This study proposes a deep-learning method for automatic pelvic fracture segmentation. Our approach consists of two consecutive networks. The anatomical segmentation network extracts left and right ilia and sacrum from CT scans. Then, the fracture segmentation network further isolates the fragments in each masked bone region.
We built a dataset containing 150 CT scans with fractured pelvis and manually annotated the fractures.
The first version of the pelvic fracture segmentation dataset has been updated. In this dataset, we provide detailed annotations of fracture segmentation for 100 patients. For each patient, we offer CT data (with the background masked out) for the left hipbone, right hipbone, and sacrum, along with their annotations.
Data Discription: DataSet for Pelvic Fracture Segmentation
Paper link: https://link.springer.com/chapter/10.1007/978-3-031-43996-4_30
TMI PENGWIN DataSet: TMI-PENGWIN DataSet
Note: We are using this dataset to host the Pelvic Bone Fragments with Injuries Segmentation (PENGWIN) Challenge in MICCAI 2024, and the remaining sets of validation and evaluation data will be released after the competition. Welcome to PENGWIN Challenge!
You can find the training and testing methods for FracSegNet at Training and inference. Additionally, we provide the latest training models for your convenience.
If you find our work is useful in your research, please consider citing:
@InProceedings{10.1007/978-3-031-43996-4_30,
author="Liu, Yanzhen and Yibulayimu, Sutuke and Sang, Yudi and Zhu, Gang and Wang, Yu and Zhao, Chunpeng and Wu, Xinbao",
title="Pelvic Fracture Segmentation Using a Multi-scale Distance-Weighted Neural Network",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="312--321",
isbn="978-3-031-43996-4"
}