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

Lookup NU author(s): Dr Xiang XieORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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 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 20.49 mm on the source domain DBT. And it outperforms the baseline models for the task in FFDM images from both the CBIS-DDSM and the FFDM-DBT dataset, achieving the average DSC of 50.78% and the average HD of 35.12 mm 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. Code will be available at

Publication metadata

Author(s): Wang K, Ye ZT, Xie X, Cui HD, Chen T, Liu BT

Publication type: Article

Publication status: Published

Journal: Knowledge-Based Systems

Year: 2024

Volume: 283

Print publication date: 11/01/2024

Online publication date: 02/11/2023

Acceptance date: 30/10/2023

Date deposited: 03/11/2023

ISSN (print): 0950-7051

ISSN (electronic): 1872-7409

Publisher: Elsevier BV


DOI: 10.1016/j.knosys.2023.111127

ePrints DOI: 10.57711/6xbh-7q09


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Funder referenceFunder name
“Ling Yan” Research and Development Project of Science and Technology Department of Zhejiang Province of China
National Science and Technology Program of China
Zhejiang Provincial Natural Science Foundation of China
Zhejiang Shuren University Basic Scientific Research Special Funds