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.

通过使用渐进双先验网络（PDPNet）分割动态增强磁共振图像 (DCE-MRI) 中具有不同来源的乳腺肿瘤，旨在提高乳腺肿瘤分割模型的泛化能力，改善对小型、低对比度和不规则形状乳腺肿瘤的分割性能。