Abstract The 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, etc. In this paper, a novel framework named MLN-net is proposed for clustered microcalcification segmentation. It can accurately segment multi-source images using only single source images. Specifically, to rich domain distribution information, we introduce a source domain image augmentation for generating multi-source images. A structure of multiple layer normalization (LN) layers is then 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. MLN-net enhances segmentation quality of full-field digital mammography (FFDM) and digital breast tomosynthe (DBT) images from the FFDM-DBT dataset, achieving the average Dice similarity coefficient (DSC) of 86.52% and the average Hausdorff distance (HD) of 20.49mm on the source domain DBT. And it outperforms the baseline models for the task in FFDM images from both the CBIS-DDSM and FFDM-DBT dataset, achieving the average DSC of 50.78% and the average HD of 35.12mm on the source domain CBIS-DDSM. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and its segmentation accuracy surpasses state-of-the-art methods.

We use FFDM-DBT and CBIS-DDSM dataset to validate the proposed MLN-net. FFDM-DBT is private dataset, and due to privacy concerns, we only show a portion of the data as toy-data to show its data characteristics. CBIS-DDSM can be obtained from the website.

We have open-sourced MLN-net's main modules' code in Version1, including the source domain data augmentation module, the multi-LN structure and the branch selection strategy. The backbone of MLN-net comes from Swinunet. The complete code is being collated and will be released soon.

Our codes are built upon CSDG, Swinunet, and Dual-Normalization, thanks for their contribution to the community and the development of researches!