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Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT),...

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Autores principales: Yue, Ning, Zhang, Jingwei, Zhao, Jing, Zhang, Qinyan, Lin, Xinshan, Yang, Jijiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9404905/
https://www.ncbi.nlm.nih.gov/pubmed/36004884
http://dx.doi.org/10.3390/bioengineering9080359
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author Yue, Ning
Zhang, Jingwei
Zhao, Jing
Zhang, Qinyan
Lin, Xinshan
Yang, Jijiang
author_facet Yue, Ning
Zhang, Jingwei
Zhao, Jing
Zhang, Qinyan
Lin, Xinshan
Yang, Jijiang
author_sort Yue, Ning
collection PubMed
description Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient’s lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient’s bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease.
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spelling pubmed-94049052022-08-26 Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN Yue, Ning Zhang, Jingwei Zhao, Jing Zhang, Qinyan Lin, Xinshan Yang, Jijiang Bioengineering (Basel) Article Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient’s lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient’s bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease. MDPI 2022-08-01 /pmc/articles/PMC9404905/ /pubmed/36004884 http://dx.doi.org/10.3390/bioengineering9080359 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yue, Ning
Zhang, Jingwei
Zhao, Jing
Zhang, Qinyan
Lin, Xinshan
Yang, Jijiang
Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN
title Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN
title_full Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN
title_fullStr Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN
title_full_unstemmed Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN
title_short Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN
title_sort detection and classification of bronchiectasis based on improved mask-rcnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9404905/
https://www.ncbi.nlm.nih.gov/pubmed/36004884
http://dx.doi.org/10.3390/bioengineering9080359
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