Accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent deep learning advancements in medical image segmentation provide insufficient contribution to practical applications, due to the domain shift resulting from differences in patient postures, individual gland density, and imaging modalities of mammography etc. In this paper, a novel framework named MLN-net, which can accurately segment multi-source images using only single source images, is proposed for clustered microcalcification segmentation. We first propose a source domain image augmentation method to generate multi-source images, leading to improved generalization. And a structure of multiple layer normalization (LN) layers is used to construct the segmentation network, which can be found efficient for clustered microcalcification segmentation in different domains. Additionally, a branch selection strategy is designed for measuring the similarity of the source domain data and the target domain data. To validate the proposed MLN-net, extensive analyses including ablation experiments are performed, comparison of 12 baseline methods. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and the its segmentation accuracy surpasses state-of-the-art methods. Code will be available at https://github.com/yezanting/MLN-NET-VERSON1.

翻译：乳腺X线摄影中簇状微钙化的精确分割对于乳腺癌的诊断与治疗至关重要。尽管近年来深度学习在医学图像分割方面已达到专家级精度，但由于患者体位差异、个体腺体密度差异及乳腺X线摄影成像模态不同等导致的域偏移问题，这些方法在实践应用中贡献不足。本文提出一种名为MLN-net的新型框架，该框架仅使用单源图像即可精确分割多源图像，专门用于簇状微钙化分割。首先，提出一种源域图像增强方法生成多源图像，从而提升模型泛化能力；其次，采用多层归一化（LN）层结构构建分割网络，并证实该方法对跨域簇状微钙化分割具有高效性；此外，设计分支选择策略以衡量源域数据与目标域数据的相似性。为验证所提MLN-net，开展了包含消融实验的全面分析，并与12种基线方法进行对比。大量实验证实了MLN-net在跨域簇状微钙化分割中的有效性，且其分割精度超越现有最优方法。源代码将发布于https://github.com/yezanting/MLN-NET-VERSON1。