Article Details

Title · Authors · Abstract · Keywords · DOI · References

A Multi-scale Wavelet Transform Network for Cardiac MRI Segmentation of MS-WTUNet

HUANG Jiamin1, ZHANG Xiaobo1,2

(1.Xi'an Shiyou University, Xi'an 710065, China; 2.Xianyang Normal University, Xianyang 712000, China)

Section：Artificial Intelligence · Vol. 2026, Issue 1 · Pages：52-57 · 2096-4706（2026）01-0052-06 · CLC number：TP391.4 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2026.01.011

Abstract

The accurate segmentation of cardiac MRI images is crucial for the diagnosis and treatment of cardiovascular diseases. However, the complex structure of the heart, blurred boundaries, and low tissue contrast pose significant challenges for traditional U-Net architectures in achieving precise segmentation. This paper proposes a Multi-Scale Wavelet Transform Network named MS-WTUNet. The network employs U-Net as its backbone, embeds wavelet blocks with attention into each layer of the encoder and decoder, and enhances texture and edge information in the frequency domain, thereby effectively improving the model's capability in characterizing complex boundaries. Furthermore, the model incorporates cross-layer multi-scale feature fusion and a hierarchical deep supervision loss, optimizing the learning process from local details to global semantics. Experiments on the public ACDC dataset demonstrate that MS-WTUNet can effectively improve the segmentation accuracy of myocardial and other boundary-ambiguous structures to 91.70%, providing a high-performance solution for automatic segmentation of cardiac MRI images.

Keywords

medical image segmentation; U-Net; Wavelet Transform; MRI image

References

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Funding

National Natural Science Foundation of China (61401383); Shaanxi Province Natural Science Basic Research Program Project (2021JM-518)

About the authors

Huang Jiamin (2002-), female, Han ethnicity, from Xianyang, Shaanxi, master's degree, currently enrolled, research interests include image processing; Zhang Xiaobo (1979-), male, Han ethnicity, from Xianyang, Shaanxi, professor, doctoral degree, research interests include image processing and natural language processing.