Deep learning (DL) applied to breast tissue segmentation in magnetic
resonance imaging (MRI) has received increased attention in the last decade,
however, the domain shift which arises from different vendors, acquisition
protocols, and biological heterogeneity, remains an important but challenging
obstacle on the path towards clinical implementation. In this paper, we propose
a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA)
framework to address this issue in an unsupervised manner. Our approach
incorporates self-training with contrastive learning to align feature
representations between domains. In particular, we extend the contrastive loss
by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid
contrasts to better exploit the underlying semantic information of the image at
different levels. To resolve the data imbalance problem, we utilize a
category-wise cross-domain sampling strategy to sample anchors from target
images and build a hybrid memory bank to store samples from source images. We
have validated MSCDA with a challenging task of cross-domain breast MRI
segmentation between datasets of healthy volunteers and invasive breast cancer
patients. Extensive experiments show that MSCDA effectively improves the
model's feature alignment capabilities between domains, outperforming
state-of-the-art methods. Furthermore, the framework is shown to be
label-efficient, achieving good performance with a smaller source dataset. The
code is publicly available at \url{https://github.com/ShengKuangCN/MSCDA}.Comment: 17 pages, 8 figure