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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 https://github.com/yezanting/MLN-NET-VERSON1.
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
URL: https://doi.org/10.1016/j.knosys.2023.111127
DOI: 10.1016/j.knosys.2023.111127
ePrints DOI: 10.57711/6xbh-7q09
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