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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...

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Autores principales: Zhao, Yawu, Wang, Shudong, Zhang, Yulin, Qiao, Sibo, Zhang, Mufei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
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.
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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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