摘要目的:利用PET本身性质构造先验，以准确分割病变区域。方法:提出一种基于测地线先验驱动PET肿瘤分割的网络框架(简称测地线网络)，即通过构建偏微分方程来刻画PET不同区域的测地线距离。以CT标签定位的肿瘤标记点作为方程的初始条件，利用光滑Heaviside函数对测地线距离进行映射，以增强肺部肿瘤或乳腺肿瘤与正常组织的对比度。网络框架采用双分支架构，利用测地线先验辅助PET图像分割。结果:所提方法在肺癌分割中，Dice系数达到94.92%。乳腺癌分割达90.12%。在Unet中加入测地线先验后，乳腺癌Dice系数上升32.37%(由42.50%上升至74.87%)。结论:测地线先验可以较好地提升分割结果并增强网络泛化能力。

abstractsObjective:To construct a prior based on the inherent properties of PET to accurately segment the lesion areas.Methods:A network framework for PET tumor segmentation driven by geodesic priors was proposed (geodesic network for short). Specifically, partial differential equations were constructed to characterize the geodesic distances between different regions in PET images. Tumor marker points identified by CT labeling were used as the initial conditions for the equations. To enhance the contrast between areas of lung or breast tumors and normal tissues, a smooth Heaviside function was utilized to map the geodesic distances. The network framework adopted a dual-branch architecture, using geodesic priors to assist in PET image segmentation.Results:The proposed method achieved a Dice coefficient of 94.92% in lung cancer segmentation and 90.12% in breast cancer segmentation. With the addition of geodesic priors in the Unet, the Dice coefficient for breast cancer increased by 32.37% (from 42.50% to 74.87%).Conclusion:Geodesic priors can significantly improve segmentation outcomes and enhance the generalization capability of the network.