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Design of lung nodules segmentation and recognition algorithm based on deep learning
BACKGROUND: Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. T...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576909/ https://www.ncbi.nlm.nih.gov/pubmed/34749636 http://dx.doi.org/10.1186/s12859-021-04234-0 |
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author | Yu, Hui Li, Jinqiu Zhang, Lixin Cao, Yuzhen Yu, Xuyao Sun, Jinglai |
author_facet | Yu, Hui Li, Jinqiu Zhang, Lixin Cao, Yuzhen Yu, Xuyao Sun, Jinglai |
author_sort | Yu, Hui |
collection | PubMed |
description | BACKGROUND: Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules. RESULTS: 3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907. CONCLUSION: The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules. |
format | Online Article Text |
id | pubmed-8576909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85769092021-11-10 Design of lung nodules segmentation and recognition algorithm based on deep learning Yu, Hui Li, Jinqiu Zhang, Lixin Cao, Yuzhen Yu, Xuyao Sun, Jinglai BMC Bioinformatics Research BACKGROUND: Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules. RESULTS: 3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907. CONCLUSION: The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules. BioMed Central 2021-11-08 /pmc/articles/PMC8576909/ /pubmed/34749636 http://dx.doi.org/10.1186/s12859-021-04234-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Yu, Hui Li, Jinqiu Zhang, Lixin Cao, Yuzhen Yu, Xuyao Sun, Jinglai Design of lung nodules segmentation and recognition algorithm based on deep learning |
title | Design of lung nodules segmentation and recognition algorithm based on deep learning |
title_full | Design of lung nodules segmentation and recognition algorithm based on deep learning |
title_fullStr | Design of lung nodules segmentation and recognition algorithm based on deep learning |
title_full_unstemmed | Design of lung nodules segmentation and recognition algorithm based on deep learning |
title_short | Design of lung nodules segmentation and recognition algorithm based on deep learning |
title_sort | design of lung nodules segmentation and recognition algorithm based on deep learning |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576909/ https://www.ncbi.nlm.nih.gov/pubmed/34749636 http://dx.doi.org/10.1186/s12859-021-04234-0 |
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