A Multi-Stage Automatic Framework for Fracture Fragments Segmentation of Zygomatic Bone and Arch

Zygomatic bone (ZB) and zygomatic arch (ZA) fractures are among the most common cranio-maxillofacial (CMF) injuries, posing significant challenges for accurate preoperative assessment and surgical planning due to the complex anatomy and high demand for facial symmetry restoration. To address these challenges, we propose a novel deep learning-based segmentation framework specifically designed for automated analysis of ZA and ZB fracture fragments. The framework consists of three major components: a 3D Faster R-CNN network to detect the ZA and ZB regions within cranial CT volumes, a 3D ConvNeXt encoder combined with a UPerNet decoder for coarse segmentation, and a lightweight 2D segmentation network for fine-grained fracture line identification. Experimental results on a dataset of 186 CT volumes demonstrate high accuracy in region detection (mAP: 90.66%) and effective ZA and ZB region segmentation (mDice: 93.52%). While the performance of fracture line segmentation was limited by the scarcity of annotated data, the proposed pipeline significantly enhances the automation and precision of preoperative planning for ZA and ZB fracture cases. This study facilitates CMF surgical planning and advances the development of autonomous and intelligent robotic surgical systems.