{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T07:35:10Z","timestamp":1778052910929,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2020YFC2005503"],"award-info":[{"award-number":["2020YFC2005503"]}]},{"name":"National Key R&amp;D Program of China","award":["2020YFC2006702"],"award-info":[{"award-number":["2020YFC2006702"]}]},{"name":"National Key R&amp;D Program of China","award":["CYXC2010"],"award-info":[{"award-number":["CYXC2010"]}]},{"name":"Collaborative Innovation Project of Beijing Chaoyang District","award":["2020YFC2005503"],"award-info":[{"award-number":["2020YFC2005503"]}]},{"name":"Collaborative Innovation Project of Beijing Chaoyang District","award":["2020YFC2006702"],"award-info":[{"award-number":["2020YFC2006702"]}]},{"name":"Collaborative Innovation Project of Beijing Chaoyang District","award":["CYXC2010"],"award-info":[{"award-number":["CYXC2010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/bioengineering9080359","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T21:01:24Z","timestamp":1659387684000},"page":"359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN"],"prefix":"10.3390","volume":"9","author":[{"given":"Ning","family":"Yue","sequence":"first","affiliation":[{"name":"Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinshan","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4124-6408","authenticated-orcid":false,"given":"Jijiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/S2213-2600(16)30320-4","article-title":"Comorbidities and the risk of mortality in patients with bronchiectasis: An international multicentre cohort study","volume":"4","author":"McDonnell","year":"2016","journal-title":"Lancet Resp. 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