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Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation

BACKGROUND: Lung cancer is a malignant tumor originating from the bronchial mucosa or glands of the lung. Early lung cancer patients often have no obvious symptoms, but early detection and treatment have an important clinical significance for prognostic effect. Computed tomography (CT) is one of the...

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Autores principales: Yang, Dewu, Du, Juan, Liu, Kang, Sui, Yan, Wang, Junying, Gai, Xinghui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200143/
https://www.ncbi.nlm.nih.gov/pubmed/37066943
http://dx.doi.org/10.3233/THC-236041
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author Yang, Dewu
Du, Juan
Liu, Kang
Sui, Yan
Wang, Junying
Gai, Xinghui
author_facet Yang, Dewu
Du, Juan
Liu, Kang
Sui, Yan
Wang, Junying
Gai, Xinghui
author_sort Yang, Dewu
collection PubMed
description BACKGROUND: Lung cancer is a malignant tumor originating from the bronchial mucosa or glands of the lung. Early lung cancer patients often have no obvious symptoms, but early detection and treatment have an important clinical significance for prognostic effect. Computed tomography (CT) is one of the important means in the diagnosis of lung cancer. In order to better solve the problem of diagnosis efficiency, and reduce the rate of misdiagnosis and missed diagnosis, computer aided diagnosis are employed in the accurate localization and segmentation of pulmonary nodules through imaging diagnostics, image processing technology, and other clinical means. OBJECTIVE: This present study was envisaged to establish an intelligent segmentation model of pulmonary nodules to improve the accuracy of early screening for lung cancer patients. METHODS: Compared with the traditional segmentation model of fully convolutional neural network, the U-Net++ algorithm based on feature-weighted integration (WI-U-Net++) effectively utilized the feature weight information, adopted the adaptive weighted method for weighted integration, and performed an intelligent segmentation of the anatomical structure and image details, which was applied in the auxiliary diagnosis of pulmonary nodules in CT images. Standard chest X-ray phantom was selected as CT scanning objects, and 30 spherical and irregular simulated nodules were built into them, respectively. CT images were collected by setting different tube voltage and noise index, and randomly included into the training set, validation set and test set at a ratio of 8:1:1. RESULTS: The experimental results showed that the segmentation accuracy of WI-U-Net++ algorithm for spheroid nodules and irregular nodules was 98.75% and 83.47%, respectively, which was better than that of U-Net and U-Net++ algorithm. In the auxiliary diagnosis, the recall rate of the WI-U-Net++ algorithm for spheroid nodules and irregular nodules was 93.47% and 84.52%, respectively. The accuracy of the benign or malignant identification was 80.27%, and the AUC was 0.9342. CONCLUSION: U-Net++ algorithm based on feature-weighted integration could improve the segmentation effect of pulmonary nodules. Especially in the case of irregular nodules with malignant signs, the accuracy of clinical diagnosis was significantly improved, and the level of differential diagnosis between benign and malignant was improved.
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spelling pubmed-102001432023-05-22 Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation Yang, Dewu Du, Juan Liu, Kang Sui, Yan Wang, Junying Gai, Xinghui Technol Health Care Research Article BACKGROUND: Lung cancer is a malignant tumor originating from the bronchial mucosa or glands of the lung. Early lung cancer patients often have no obvious symptoms, but early detection and treatment have an important clinical significance for prognostic effect. Computed tomography (CT) is one of the important means in the diagnosis of lung cancer. In order to better solve the problem of diagnosis efficiency, and reduce the rate of misdiagnosis and missed diagnosis, computer aided diagnosis are employed in the accurate localization and segmentation of pulmonary nodules through imaging diagnostics, image processing technology, and other clinical means. OBJECTIVE: This present study was envisaged to establish an intelligent segmentation model of pulmonary nodules to improve the accuracy of early screening for lung cancer patients. METHODS: Compared with the traditional segmentation model of fully convolutional neural network, the U-Net++ algorithm based on feature-weighted integration (WI-U-Net++) effectively utilized the feature weight information, adopted the adaptive weighted method for weighted integration, and performed an intelligent segmentation of the anatomical structure and image details, which was applied in the auxiliary diagnosis of pulmonary nodules in CT images. Standard chest X-ray phantom was selected as CT scanning objects, and 30 spherical and irregular simulated nodules were built into them, respectively. CT images were collected by setting different tube voltage and noise index, and randomly included into the training set, validation set and test set at a ratio of 8:1:1. RESULTS: The experimental results showed that the segmentation accuracy of WI-U-Net++ algorithm for spheroid nodules and irregular nodules was 98.75% and 83.47%, respectively, which was better than that of U-Net and U-Net++ algorithm. In the auxiliary diagnosis, the recall rate of the WI-U-Net++ algorithm for spheroid nodules and irregular nodules was 93.47% and 84.52%, respectively. The accuracy of the benign or malignant identification was 80.27%, and the AUC was 0.9342. CONCLUSION: U-Net++ algorithm based on feature-weighted integration could improve the segmentation effect of pulmonary nodules. Especially in the case of irregular nodules with malignant signs, the accuracy of clinical diagnosis was significantly improved, and the level of differential diagnosis between benign and malignant was improved. IOS Press 2023-04-28 /pmc/articles/PMC10200143/ /pubmed/37066943 http://dx.doi.org/10.3233/THC-236041 Text en © 2023 – The authors. Published by IOS Press. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yang, Dewu
Du, Juan
Liu, Kang
Sui, Yan
Wang, Junying
Gai, Xinghui
Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation
title Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation
title_full Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation
title_fullStr Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation
title_full_unstemmed Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation
title_short Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation
title_sort construction of u-net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200143/
https://www.ncbi.nlm.nih.gov/pubmed/37066943
http://dx.doi.org/10.3233/THC-236041
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