  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 and 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 centers. 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 and HD95 of PDPNet were improved at least by 5.13%
and 7.58% respectively on multi-center test sets. 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
irregular tumors. Thus integrating them in a unified framework improved the
multi-center breast tumor segmentation performance. The source code and open
data can be accessed at https://github.com/wangli100209/PDPNet.
