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MFR‐UNet: A Medical Image Segmentation Network With Fused Multi‐Scale Feature Refinement
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Guiling Shi,
Shuo Sun,
Yuchen Wang,
Yulin Zhang ,
Weixian Li,
Yawu Zhao,
Cheng Cheng
IET Systems Biology, Volume: 20, Issue: 1
Swansea University Author:
Cheng Cheng
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DOI (Published version): 10.1049/syb2.70049
Abstract
Medical image segmentation is crucial for clinical diagnosis and treatment planning. Although methods based on CNN, particularly U-Net and its variants, have achieved remarkable success in automated segmentation tasks, they still face challenges in effectively capturing long-range dependencies, refi...
| Published in: | IET Systems Biology |
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| ISSN: | 1751-8849 1751-8857 |
| Published: |
Institution of Engineering and Technology (IET)
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71127 |
| Abstract: |
Medical image segmentation is crucial for clinical diagnosis and treatment planning. Although methods based on CNN, particularly U-Net and its variants, have achieved remarkable success in automated segmentation tasks, they still face challenges in effectively capturing long-range dependencies, refining multi-level features, and efficiently integrating cross-level information. To address these issues, we propose a novel U-Net architecture incorporating a multi-scale feature refinement mechanism (MFR-UNet). This network enhances segmentation accuracy and robustness by integrating three innovative modules. First, we designed a wavelet transform convolution (WtConv) module. By decomposing, processing, and reconstructing features in the frequency domain, this module enables the model to learn high-frequency details and low-frequency contours with greater precision. Second, we introduce a large receptive field attention (LRFA) module in the encoder. Combining deep separable convolutions with multi-head attention, LRFA efficiently captures global contextual information at low computational cost. Finally, in the skip connections and decoding path, our weighted contextual fusion module (WCF) module dynamically generates channel attention weights for one feature stream to another, achieving efficient adaptive feature fusion. Simulation experiments on multiple public medical image segmentation datasets demonstrate that our MFR-UNet outperforms several existing mainstream methods in key metrics such as Dice coefficient and IoU, proving its effectiveness in enhancing segmentation accuracy and boundary clarity. |
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| Keywords: |
feature fusion; large receptive field; medical image segmentation; wavelet |
| College: |
Faculty of Science and Engineering |
| Funders: |
Doctoral Training Centre at Swansea University (Grant Number: RS718);
UKRI (Grant Number: EP/W020408/1);
Hebei Province Education Science ”14th Five-Year Plan” (Grant Number: 2303065) |
| Issue: |
1 |