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}.

翻译：[翻译摘要] 近十年来，深度学习在乳腺磁共振成像组织分割中的应用日益受到关注，然而由不同设备厂商、采集方案及生物异质性引发的域偏移问题，仍是阻碍其临床实施的重要且具有挑战性的障碍。本文提出一种新颖的多层级语义引导对比域适应框架，以无监督方式解决该问题。该方法将自训练与对比学习相结合，实现跨域特征表征对齐。特别地，我们通过引入像素级、像素-质心级及质心-质心级对比扩展了对比损失函数，从而更好地挖掘图像中不同层级的潜在语义信息。为解决数据不平衡问题，我们采用类别感知的跨域采样策略从目标图像中采样锚点，并构建混合记忆库存储源图像样本。我们在健康志愿者与浸润性乳腺癌患者数据集间的跨域乳腺MRI分割任务中验证了MSCDA框架。大量实验表明，MSCDA有效增强了模型域间特征对齐能力，其性能优于现有最先进方法。此外，该框架展现出标签高效性，在较小源数据集下仍能实现良好性能。代码已开源在\url{https://github.com/ShengKuangCN/MSCDA}。