Edge-Enhanced Calcaneal Fracture Segmentation Using a Multi-Task CNN-Transformer Hybrid Network

Traditional fracture diagnosis relies heavily on the experience of clinicians and the interpretation of medical imaging. In complex cases, the inefficiency of manual interpretation often leads to misdiagnosis or missed detection, underscoring the need for automated segmentation techniques. A major challenge in calcaneal fracture image segmentation lies in the blurred and irregular boundaries of fractures, coupled with the scarcity of high-quality annotated data. To address these issues, this study independently constructs the first dataset specifically dedicated to Calcaneal Fracture segmentation, termed CalFrac. This dataset, collected from Ruijin Hospital in Shanghai, comprises CT scans of calcaneal fractures from 139 patients, along with corresponding pixel-level annotated ground truth segmentation masks. In addition, we propose the Calcaneal Fracture segmentation-Edge detection Network (CFE-Net), a multi-task CNN-Transformer hybrid architecture that employs a dual-branch structure to jointly perform fracture segmentation and edge detection. The main segmentation network adopts an encoder–decoder design to localize the fracture region, while the edge detection branch extracts boundary information and refines the segmentation via cross-branch feature interaction. Experiments on the CalFrac dataset compare CFE-Net with eight state-of-the-art methods. CFE-Net achieves superior performance across all evaluation metrics, demonstrating its advantages in both region integrity and boundary delineation.