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Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net

Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients' rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based...

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Autores principales: Wang, Shuai, Jiang, Zhengwei, Yang, Hualin, Li, Xiangrong, Yang, Zhicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9492365/
https://www.ncbi.nlm.nih.gov/pubmed/36156962
http://dx.doi.org/10.1155/2022/4259471
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author Wang, Shuai
Jiang, Zhengwei
Yang, Hualin
Li, Xiangrong
Yang, Zhicheng
author_facet Wang, Shuai
Jiang, Zhengwei
Yang, Hualin
Li, Xiangrong
Yang, Zhicheng
author_sort Wang, Shuai
collection PubMed
description Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients' rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based on Attention U-Net, the attention module based on multilevel feature map fusion is adopted, two residual modules are introduced instead of the original convolution blocks. a hybrid loss function is used for prediction during the training process, and finally, the image superposition process is realized. In this experiment, we expanded 420 lumbar MRI images of 180 patients to 1000 images and trained them by different algorithms, respectively, and accuracy, recall, and Dice similarity coefficient metrics were used to analyze these algorithms. The results show that compared with SVM, FCN, R-CNN, U-Net, and Attention U-Net models, the improved model achieved better results in all three evaluations, with 95.50%, 94.53%, and 95.01%, respectively, which proves the better performance of the proposed method for segmentation in lumbar disc and caudal vertebrae.
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spelling pubmed-94923652022-09-22 Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net Wang, Shuai Jiang, Zhengwei Yang, Hualin Li, Xiangrong Yang, Zhicheng Comput Intell Neurosci Research Article Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients' rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based on Attention U-Net, the attention module based on multilevel feature map fusion is adopted, two residual modules are introduced instead of the original convolution blocks. a hybrid loss function is used for prediction during the training process, and finally, the image superposition process is realized. In this experiment, we expanded 420 lumbar MRI images of 180 patients to 1000 images and trained them by different algorithms, respectively, and accuracy, recall, and Dice similarity coefficient metrics were used to analyze these algorithms. The results show that compared with SVM, FCN, R-CNN, U-Net, and Attention U-Net models, the improved model achieved better results in all three evaluations, with 95.50%, 94.53%, and 95.01%, respectively, which proves the better performance of the proposed method for segmentation in lumbar disc and caudal vertebrae. Hindawi 2022-09-14 /pmc/articles/PMC9492365/ /pubmed/36156962 http://dx.doi.org/10.1155/2022/4259471 Text en Copyright © 2022 Shuai Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Shuai
Jiang, Zhengwei
Yang, Hualin
Li, Xiangrong
Yang, Zhicheng
Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net
title Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net
title_full Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net
title_fullStr Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net
title_full_unstemmed Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net
title_short Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net
title_sort automatic segmentation of lumbar spine mri images based on improved attention u-net
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9492365/
https://www.ncbi.nlm.nih.gov/pubmed/36156962
http://dx.doi.org/10.1155/2022/4259471
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