To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast amd irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different sites. The PDPNet first cropped tumor regions with a coarse-segmentation based localization module, then the breast tumor mask was progressively refined by using the weak semantic priori and cross-scale correlation prior knowledge. To validate the effectiveness of PDPNet, we compared it with several state-of-the-art methods on multi-center datasets. The results showed that, comparing against the suboptimal method, the DSC, SEN, KAPPA and HD95 of PDPNet were improved 3.63\%, 8.19\%, 5.52\%, and 3.66\% respectively. In addition, through ablations, we demonstrated that the proposed localization module can decrease the influence of normal tissues and therefore improve the generalization ability of the model. The weak semantic priors allow focusing on tumor regions to avoid missing small tumors and low-contrast tumors. The cross-scale correlation priors are beneficial for promoting the shape-aware ability for irregual tumors. Thus integrating them in a unified framework improved the multi-center breast tumor segmentation performance.

翻译：为提升乳腺肿瘤分割模型的泛化能力，并改善对小尺寸、低对比度及不规则形态乳腺肿瘤的分割性能，我们提出一种渐进式双重先验网络（PDPNet），用于对多中心采集的动态增强磁共振图像（DCE-MRI）中的乳腺肿瘤进行分割。该网络首先通过基于粗分割的定位模块裁剪肿瘤区域，随后利用弱语义先验与跨尺度相关性先验知识逐步细化乳腺肿瘤掩膜。为验证PDPNet的有效性，我们在多中心数据集上将其与多种先进方法进行了对比。结果表明，与次优方法相比，PDPNet的DSC、SEN、KAPPA和HD95分别提升了3.63%、8.19%、5.52%和3.66%。此外，通过消融实验，我们证明了所提出的定位模块能够降低正常组织的影响，从而提升模型泛化能力；弱语义先验可聚焦于肿瘤区域，避免遗漏小肿瘤和低对比度肿瘤；跨尺度相关性先验有助于增强不规则肿瘤的形状感知能力。因此，将这些模块集成于统一框架中可提升多中心乳腺肿瘤分割性能。