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WRANet: wavelet integrated residual attention U-Net network for medical image segmentation
Medical image segmentation is crucial for the diagnosis and analysis of disease. Deep convolutional neural network methods have achieved great success in medical image segmentation. However, they are highly susceptible to noise interference during the propagation of the network, where weak noise can...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248349/ https://www.ncbi.nlm.nih.gov/pubmed/37361970 http://dx.doi.org/10.1007/s40747-023-01119-y |
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author | Zhao, Yawu Wang, Shudong Zhang, Yulin Qiao, Sibo Zhang, Mufei |
author_facet | Zhao, Yawu Wang, Shudong Zhang, Yulin Qiao, Sibo Zhang, Mufei |
author_sort | Zhao, Yawu |
collection | PubMed |
description | Medical image segmentation is crucial for the diagnosis and analysis of disease. Deep convolutional neural network methods have achieved great success in medical image segmentation. However, they are highly susceptible to noise interference during the propagation of the network, where weak noise can dramatically alter the network output. As the network deepens, it can face problems such as gradient explosion and vanishing. To improve the robustness and segmentation performance of the network, we propose a wavelet residual attention network (WRANet) for medical image segmentation. We replace the standard downsampling modules (e.g., maximum pooling and average pooling) in CNNs with discrete wavelet transform, decompose the features into low- and high-frequency components, and remove the high-frequency components to eliminate noise. At the same time, the problem of feature loss can be effectively addressed by introducing an attention mechanism. The combined experimental results show that our method can effectively perform aneurysm segmentation, achieving a Dice score of 78.99%, an IoU score of 68.96%, a precision of 85.21%, and a sensitivity score of 80.98%. In polyp segmentation, a Dice score of 88.89%, an IoU score of 81.74%, a precision rate of 91.32%, and a sensitivity score of 91.07% were achieved. Furthermore, our comparison with state-of-the-art techniques demonstrates the competitiveness of the WRANet network. |
format | Online Article Text |
id | pubmed-10248349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-102483492023-06-12 WRANet: wavelet integrated residual attention U-Net network for medical image segmentation Zhao, Yawu Wang, Shudong Zhang, Yulin Qiao, Sibo Zhang, Mufei Complex Intell Systems Original Article Medical image segmentation is crucial for the diagnosis and analysis of disease. Deep convolutional neural network methods have achieved great success in medical image segmentation. However, they are highly susceptible to noise interference during the propagation of the network, where weak noise can dramatically alter the network output. As the network deepens, it can face problems such as gradient explosion and vanishing. To improve the robustness and segmentation performance of the network, we propose a wavelet residual attention network (WRANet) for medical image segmentation. We replace the standard downsampling modules (e.g., maximum pooling and average pooling) in CNNs with discrete wavelet transform, decompose the features into low- and high-frequency components, and remove the high-frequency components to eliminate noise. At the same time, the problem of feature loss can be effectively addressed by introducing an attention mechanism. The combined experimental results show that our method can effectively perform aneurysm segmentation, achieving a Dice score of 78.99%, an IoU score of 68.96%, a precision of 85.21%, and a sensitivity score of 80.98%. In polyp segmentation, a Dice score of 88.89%, an IoU score of 81.74%, a precision rate of 91.32%, and a sensitivity score of 91.07% were achieved. Furthermore, our comparison with state-of-the-art techniques demonstrates the competitiveness of the WRANet network. Springer International Publishing 2023-06-08 /pmc/articles/PMC10248349/ /pubmed/37361970 http://dx.doi.org/10.1007/s40747-023-01119-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Zhao, Yawu Wang, Shudong Zhang, Yulin Qiao, Sibo Zhang, Mufei WRANet: wavelet integrated residual attention U-Net network for medical image segmentation |
title | WRANet: wavelet integrated residual attention U-Net network for medical image segmentation |
title_full | WRANet: wavelet integrated residual attention U-Net network for medical image segmentation |
title_fullStr | WRANet: wavelet integrated residual attention U-Net network for medical image segmentation |
title_full_unstemmed | WRANet: wavelet integrated residual attention U-Net network for medical image segmentation |
title_short | WRANet: wavelet integrated residual attention U-Net network for medical image segmentation |
title_sort | wranet: wavelet integrated residual attention u-net network for medical image segmentation |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248349/ https://www.ncbi.nlm.nih.gov/pubmed/37361970 http://dx.doi.org/10.1007/s40747-023-01119-y |
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