MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization

TL;DR

MLN-net is a novel deep learning framework that enhances the segmentation of clustered microcalcifications in mammography across different domains by using source domain augmentation and multiple layer normalization.

Contribution

The paper introduces MLN-net, which employs source image augmentation and multiple layer normalization to improve multi-source microcalcification segmentation from single source images.

Findings

MLN-net outperforms 12 baseline methods in segmentation accuracy.

The proposed method generalizes well across different domains.

Ablation studies confirm the effectiveness of each component.

Abstract

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.