Fracture interactive geodesic active contours for bone segmentation

• URL: http://arxiv.org/abs/2509.14817v1
• Date: Thu, 18 Sep 2025 10:18:29 GMT
• Title: Fracture interactive geodesic active contours for bone segmentation
• Authors: Liheng Wang, Licheng Zhang, Hailin Xu, Jingxin Zhao, Xiuyun Su, Jiantao Li, Miutian Tang, Weilu Gao, Chong Chen,
• Abstract summary: We propose a fracture interactive geodesic active contour algorithm tailored for bone segmentation.<n>Inspired by orthopedic knowledge, we construct a novel edge-detector function that guides the contour towards bone edges.<n>Our algorithm also provides insights into combining the domain knowledge and deep neural networks.
• Score: 10.955555620402796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For bone segmentation, the classical geodesic active contour model is usually limited by its indiscriminate feature extraction, and then struggles to handle the phenomena of edge obstruction, edge leakage and bone fracture. Thus, we propose a fracture interactive geodesic active contour algorithm tailored for bone segmentation, which can better capture bone features and perform robustly to the presence of bone fractures and soft tissues. Inspired by orthopedic knowledge, we construct a novel edge-detector function that combines the intensity and gradient norm, which guides the contour towards bone edges without being obstructed by other soft tissues and therefore reduces mis-segmentation. Furthermore, distance information, where fracture prompts can be embedded, is introduced into the contour evolution as an adaptive step size to stabilize the evolution and help the contour stop at bone edges and fractures. This embedding provides a way to interact with bone fractures and improves the accuracy in the fracture regions. Experiments in pelvic and ankle segmentation demonstrate the effectiveness on addressing the aforementioned problems and show an accurate, stable and consistent performance, indicating a broader application in other bone anatomies. Our algorithm also provides insights into combining the domain knowledge and deep neural networks.

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