Pelvic fractures, often caused by high-impact trauma, frequently require surgical intervention. Imaging techniques such as CT and 2D X-ray imaging are used to transfer the surgical plan to the operating room through image registration, enabling quick intraoperative adjustments. Specifically, segmenting pelvic fractures from 2D X-ray imaging can assist in accurately positioning bone fragments and guiding the placement of screws or metal plates. In this study, we propose a novel deep learning-based category and fragment segmentation (CFS) framework for the automatic segmentation of pelvic bone fragments in 2D X-ray images. The framework consists of three consecutive steps: category segmentation, fragment segmentation, and post-processing. Our best model achieves an IoU of 0.91 for anatomical structures and 0.78 for fracture segmentation. Results demonstrate that the CFS framework is effective and accurate.
@article{arxiv.2504.11872,
title = {A Category-Fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images},
author = {Daiqi Liu and Fuxin Fan and Andreas Maier},
journal= {arXiv preprint arXiv:2504.11872},
year = {2025}
}