{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:56:54Z","timestamp":1776301014701,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020AAA0109400"],"award-info":[{"award-number":["2020AAA0109400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023ZD0506200"],"award-info":[{"award-number":["2023ZD0506200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023ZD0506204"],"award-info":[{"award-number":["2023ZD0506204"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024ZD0540900"],"award-info":[{"award-number":["2024ZD0540900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Mycoses"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The differential diagnosis of invasive pulmonary aspergillosis (IPA), pulmonary mucormycosis (PM), bacterial pneumonia (BP) and pulmonary tuberculosis (PTB) are challenging due to overlapping clinical and imaging features. Manual CT lesion segmentation is time\u2010consuming, deep\u2010learning (DL)\u2010based segmentation models offer a promising solution, yet disease\u2010specific models for these infections remain underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>We aimed to develop and validate dedicated CT segmentation models for IPA, PM, BP and PTB to enhance diagnostic accuracy.<\/jats:p>\n                    <jats:p>Methods\uff1aRetrospective multi\u2010centre data (115 IPA, 53 PM, 130 BP, 125 PTB) were used for training\/internal validation, with 21 IPA, 8PM, 30 BP and 31 PTB cases for external validation. Expert\u2010annotated lesions served as ground truth. An improved 3D U\u2010Net architecture was employed for segmentation, with preprocessing steps including normalisations, cropping and data augmentation. Performance was evaluated using Dice coefficients.<\/jats:p>\n                    <jats:p>Results\uff1aInternal validation achieved Dice scores of 78.83% (IPA), 93.38% (PM), 80.12% (BP) and 90.47% (PTB). External validation showed slightly reduced but robust performance: 75.09% (IPA), 77.53% (PM), 67.40% (BP) and 80.07% (PTB). The PM model demonstrated exceptional generalisability, scoring 83.41% on IPA data. Cross\u2010validation revealed mutual applicability, with IPA\/PTB models achieving &gt;\u200975% Dice for each other's lesions. BP segmentation showed lower but clinically acceptable performance ( \uff1e72%), likely due to complex radiological patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Disease\u2010specific DL segmentation models exhibited high accuracy, particularly for PM and PTB. While IPA and BP models require refinement, all demonstrated cross\u2010disease utility, suggesting immediate clinical value for preliminary lesion annotation. Future efforts should enhance datasets and optimise models for intricate cases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/myc.70084","type":"journal-article","created":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T05:58:49Z","timestamp":1751090329000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning Models for\n                    <scp>CT<\/scp>\n                    Segmentation of Invasive Pulmonary Aspergillosis, Mucormycosis, Bacterial Pneumonia and Tuberculosis: A Multicentre Study"],"prefix":"10.1111","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8101-0192","authenticated-orcid":false,"given":"Yun","family":"Li","sequence":"first","affiliation":[{"name":"National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7011-7312","authenticated-orcid":false,"given":"Feifei","family":"Huang","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8141-662X","authenticated-orcid":false,"given":"Deyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Neusoft Research of Intelligent Healthcare Technology, Co. Ltd  Shenyang China"},{"name":"Neusoft Corporation  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Neurology Gaozhou People's Hospital  Gaozhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Neusoft Research of Intelligent Healthcare Technology, Co. Ltd  Shenyang China"},{"name":"Neusoft Corporation  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Liang","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junnan","family":"Pan","sequence":"additional","affiliation":[{"name":"Neusoft Research of Intelligent Healthcare Technology, Co. Ltd  Shenyang China"},{"name":"Neusoft Corporation  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lunfang","family":"Tan","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyi","family":"Liu","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Lin","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengtu","family":"Li","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guodong","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine The Tenth Affiliated Hospital of Southern Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology The Second Affiliated Hospital of Guangzhou Medical University  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengbao","family":"Peng","sequence":"additional","affiliation":[{"name":"Neusoft Research of Intelligent Healthcare Technology, Co. 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