  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.
