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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MDPI AG
2022-08-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/8/359 |
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| author | Ning Yue Jingwei Zhang Jing Zhao Qinyan Zhang Xinshan Lin Jijiang Yang |
| author_facet | Ning Yue Jingwei Zhang Jing Zhao Qinyan Zhang Xinshan Lin Jijiang Yang |
| author_sort | Ning Yue |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T10:01:50Z |
| format | Article |
| id | doaj.art-b1314d3bc7e84e21b252ce76307cc082 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T10:01:50Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-b1314d3bc7e84e21b252ce76307cc0822023-12-01T23:25:02ZengMDPI AGBioengineering2306-53542022-08-019835910.3390/bioengineering9080359Detection and Classification of Bronchiectasis Based on Improved Mask-RCNNNing Yue0Jingwei Zhang1Jing Zhao2Qinyan Zhang3Xinshan Lin4Jijiang Yang5Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaDepartment of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaDepartment of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, ChinaDepartment of Automation, Tsinghua University, Beijing 100084, ChinaBronchiectasis 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.https://www.mdpi.com/2306-5354/9/8/359bronchiectasisLDCTautomated scoringobject detectionMask R-CNNdecision support system |
| spellingShingle | Ning Yue Jingwei Zhang Jing Zhao Qinyan Zhang Xinshan Lin Jijiang Yang Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN Bioengineering bronchiectasis LDCT automated scoring object detection Mask R-CNN decision support system |
| 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 | bronchiectasis LDCT automated scoring object detection Mask R-CNN decision support system |
| url | https://www.mdpi.com/2306-5354/9/8/359 |
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