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线摄影中聚类微钙化的精确分割对乳腺癌诊断与治疗至关重要。尽管近期深度学习在医学图像分割领域展现出专家级精度，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因素导致的域偏移，这些进展对实际应用的贡献仍显不足。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实现多源图像的精确分割，用于聚类微钙化分割。首先提出源域图像增广方法以生成多源图像，从而提高泛化能力；其次采用多层归一化层结构构建分割网络，该结构在不同域中的聚类微钙化分割任务上表现高效；此外设计分支选择策略，用于度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在内的全面分析，并与12种基线方法进行对比。大量实验证明了MLN-net在跨域分割聚类微钙化方面的有效性，其分割精度超越现有最先进方法。代码将发布于https://github.com/yezanting/MLN-NET-VERSON1。