Global Adaptive Histogram Feature Network for Automatic Segmentation of Infection Regions in CT Images

Global Adaptive Histogram Feature Network for Automatic Segmentation of Infection Regions in CT Images | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Adaptive Histogram Feature Network for Automatic Segmentation of Infection Regions in CT Images Xinren Min, Yang Liu, Shengjing Zhou, Li Zhang, Xiaojun Gong, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3733137/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract We applied deep learning techniques in lung CT image diagnosis of COVID-19 for accurate segmentation of disease diagnosis. We propose a new deep learning framework, GAHFNet, specifically designed for automatic segmentation of COVID-19 lung CT images. GAHFNet outperforms other traditional and the state-of-the-art methods in various evaluation metrics, demonstrating the effectiveness and the efficiency of the proposed method in this task. This article discusses the limitations of current diagnostic methods, such as RT-PCR, and highlights the advantages of deep learning, including its ability to automatically learn features and handle complex lesion morphology and texture. Furthermore, the proposed method addresses the challenges in lung CT image segmentation, such as the complex image structure and difficulties of distinguishing COVID-19 pneumonia lesions from other pathologies. We provide the detailed description of the proposed GAHFNet. Finally, comprehensive experiments are carried out to evaluate the performance of GAHFNet, demonstrating that GAHFNet is able to facilitate the application of artificial intelligence in COVID-19 diagnosis and achieve accurate automatic segmentation of infected areas in COVID-19 lung CT images. COVID-19 Deep learning Feature learning Automatic segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Mar, 2024 Reviews received at journal 29 Feb, 2024 Reviewers agreed at journal 18 Feb, 2024 Reviews received at journal 25 Jan, 2024 Reviewers agreed at journal 02 Jan, 2024 Reviewers invited by journal 02 Jan, 2024 Editor assigned by journal 24 Dec, 2023 Submission checks completed at journal 11 Dec, 2023 First submitted to journal 10 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3733137","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":258245529,"identity":"a5f40dfd-a314-40ef-a96f-c4e29f7d894a","order_by":0,"name":"Xinren Min","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinren","middleName":"","lastName":"Min","suffix":""},{"id":258245530,"identity":"702a8c92-4611-4abc-97c0-f9e15d659c4f","order_by":1,"name":"Yang Liu","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":258245531,"identity":"a220477b-bb06-4bdc-baf0-8b416eb3f23a","order_by":2,"name":"Shengjing Zhou","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shengjing","middleName":"","lastName":"Zhou","suffix":""},{"id":258245532,"identity":"bcc8aa3a-fc1a-4360-8e1b-98353c7d0b49","order_by":3,"name":"Li Zhang","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":258245533,"identity":"5654d3e2-8164-4ea0-b2cd-68c11678ec3f","order_by":4,"name":"Xiaojun Gong","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Gong","suffix":""},{"id":258245534,"identity":"11ae932c-4349-43f0-bdcd-4d40fe83ceb9","order_by":5,"name":"Dongshan Yang","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongshan","middleName":"","lastName":"Yang","suffix":""},{"id":258245535,"identity":"5b3c4f8b-8fea-4173-a818-89fd3bbf7a86","order_by":6,"name":"Huihua Huang","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huihua","middleName":"","lastName":"Huang","suffix":""},{"id":258245536,"identity":"f7e67c29-690d-46b6-a3a7-b75286cc2197","order_by":7,"name":"Menghao Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Menghao","middleName":"","lastName":"Wang","suffix":""},{"id":258245537,"identity":"cb824016-9c5c-4124-9926-bc057c865c6b","order_by":8,"name":"Rui Yang","email":"","orcid":"","institution":"Chongqing College of Electronic Engineering","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Yang","suffix":""},{"id":258245538,"identity":"881693db-cc01-43a4-8ece-f8307a2b64b2","order_by":9,"name":"Mingyang Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYHACNhAhh8QmUosx6VoSG4jWojsj/dmDjztq0+fPyDFg+FB2mIF/dgN+LWY3EtINZ545nrvhRo4B44xzhxkk7hwgqOWYNG/bsdwN0jkGzLxthxkMJBIIaUlsA2lJl58N1PKXOC3JbEAtNQkMt4FaGInScuYZm+TMtgOGG+4/KzjYcy6dR+IGIS3H059JfGyrk5fvObzxwY8yazn+GQS0MAiAFRwGsw8AMQ8B9UDAD1LHUEdY4SgYBaNgFIxcAAAAh0UHG71bgwAAAABJRU5ErkJggg==","orcid":"","institution":"Southwest University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mingyang","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2023-12-10 07:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3733137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3733137/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":48062456,"identity":"53b605d1-d701-4804-8699-731ad260451a","added_by":"auto","created_at":"2023-12-12 14:28:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4108902,"visible":true,"origin":"","legend":"","description":"","filename":"GlobalAdaptiveHistogramFeatureNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3733137/v1_covered_df820113-4ed1-4d2d-b251-506cd3db9285.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Adaptive Histogram Feature Network for Automatic Segmentation of Infection Regions in CT Images","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19, Deep learning, Feature learning, Automatic segmentation","lastPublishedDoi":"10.21203/rs.3.rs-3733137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3733137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe applied deep learning techniques in lung CT image diagnosis of COVID-19 for accurate segmentation of disease diagnosis. We propose a new deep learning framework, GAHFNet, specifically designed for automatic segmentation of COVID-19 lung CT images. GAHFNet outperforms other traditional and the state-of-the-art methods in various evaluation metrics, demonstrating the effectiveness and the efficiency of the proposed method in this task. This article discusses the limitations of current diagnostic methods, such as RT-PCR, and highlights the advantages of deep learning, including its ability to automatically learn features and handle complex lesion morphology and texture. Furthermore, the proposed method addresses the challenges in lung CT image segmentation, such as the complex image structure and difficulties of distinguishing COVID-19 pneumonia lesions from other pathologies. We provide the detailed description of the proposed GAHFNet. Finally, comprehensive experiments are carried out to evaluate the performance of GAHFNet, demonstrating that GAHFNet is able to facilitate the application of artificial intelligence in COVID-19 diagnosis and achieve accurate automatic segmentation of infected areas in COVID-19 lung CT images.\u003c/p\u003e","manuscriptTitle":"Global Adaptive Histogram Feature Network for Automatic Segmentation of Infection Regions in CT Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-12 14:25:38","doi":"10.21203/rs.3.rs-3733137/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-09T02:39:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-01T00:02:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3b110935-399b-40c3-8dca-5b18d1dbf8bf","date":"2024-02-19T02:40:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-25T09:36:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"625e9ae4-bc32-466b-8837-6b29a7f5ec66","date":"2024-01-03T01:41:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-03T01:29:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-25T04:41:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-11T05:02:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Multimedia Systems","date":"2023-12-10T07:48:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"44c331d8-7be6-4a43-bdc6-d882e8213a1b","owner":[],"postedDate":"December 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-12T02:48:29+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-12 14:25:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3733137","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3733137","identity":"rs-3733137","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}