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.
Abstract (translated)
对于骨骼分割，传统的几何主动轮廓模型通常受限于其无差别特征提取，并且难以处理边缘阻塞、边缘泄漏和骨裂等现象。为此，我们提出了一种针对骨骼分割的骨折交互式几何主动轮廓算法，该算法能够更好地捕捉骨骼特征并在存在骨折和软组织的情况下表现出色。 受到正畸学知识的启发，我们构建了一个新型边缘检测函数，结合了强度和梯度范数，引导轮廓向骨骼边缘移动而不被其他软组织阻碍，从而减少误分割的情况。此外，我们将距离信息引入轮廓演化过程中作为自适应步长，这些信息中可以嵌入骨折提示，以稳定演化进程并帮助轮廓在骨骼边缘和骨折处停止。这种嵌入提供了与骨裂交互的方法，并提高了骨折区域的准确性。 实验表明，在盆腔和踝部分割中的应用证明了该算法能够有效解决上述问题，并表现出准确、稳定且一致的性能，这预示着它在其他骨骼解剖学中也具有更广泛的应用前景。我们的算法还为将领域知识与深度神经网络相结合提供了新的见解。
URL
https://arxiv.org/abs/2509.14817
