Cascaded Hybrid Models for Brain Tumor MRI Segmentation under limited dataset conditions

Cascaded Hybrid Models for Brain Tumor MRI Segmentation under limited dataset conditions | 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 Cascaded Hybrid Models for Brain Tumor MRI Segmentation under limited dataset conditions Wenyang Yang, Steven Kwok Keung Chow, Jierui Kang, ZhiMing Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6455496/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Problem Limited MRI conditions and datasets pose challenges, and the performance remains unknown in testing deep learning models, particularly in brain tumor segmentation. Purpose This study aims to investigate the efficiency, feasibility and effectiveness of integrating a Cascaded U-Sharp network hybrid model using limited MRI conditions and datasets. Methods A Cascaded hybrid model was developed using T1w and FLAIR for the pre-segmentation module with 3D U-Net. The final segmentation module was carried out using pre_results, T1w, FLAIR, T2w, and T1w+C as inputs with residual U-Net. The hybrid model underwent rigorous training and validation using the BraTS2021 datasets, followed by testing with the UPenn-GBM datasets. Moreover, the study conducted a comparative analysis of the proposed model's efficacy with different combinations of inputs using the UPenn-GBM dataset. Results The segmentation accuracy of residual U-Net on the full dataset was evaluated on DICE values of 85.62 (ET), 89.96 (TC), and 92.01 (WT) on the BraTS2021 dataset. Under a limited T2w and FLAIR dataset, the performance was 90.22 (ET), 91.77 (TC), and 92.78 (WT). Conclusion This study proposes a model for brain tumor segmentation. An experiment conducted on comparable and challenging datasets demonstrated better performance using both the full dataset and limited data available. Cascaded model UNet Deep learning Brain tumor Segmentation Limited dataset MRI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 14 Aug, 2025 Editor invited by journal 24 Jul, 2025 Editor assigned by journal 17 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 16 Jun, 2025 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. 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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-6455496","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503664932,"identity":"618339f7-a912-49c9-90b6-f1c97448e6de","order_by":0,"name":"Wenyang Yang","email":"","orcid":"","institution":"Xi'an Shiyou University","correspondingAuthor":false,"prefix":"","firstName":"Wenyang","middleName":"","lastName":"Yang","suffix":""},{"id":503664933,"identity":"47c18c03-6824-45db-80ef-332c2cbe7fa9","order_by":1,"name":"Steven Kwok Keung 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cascaded model, UNet, Deep learning, Brain tumor Segmentation, Limited dataset, MRI","lastPublishedDoi":"10.21203/rs.3.rs-6455496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6455496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eProblem \u003c/strong\u003eLimited MRI conditions and datasets pose challenges, and the \u0026nbsp;performance remains unknown in testing deep learning models, particularly in brain tumor segmentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eThis study aims to investigate the efficiency, feasibility and effectiveness of integrating a Cascaded U-Sharp network hybrid model using limited MRI conditions and datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA Cascaded hybrid model was developed using T1w and FLAIR for the pre-segmentation module with 3D U-Net. The final segmentation module was carried out using pre_results, T1w, FLAIR, T2w, and T1w+C as inputs with residual U-Net. The hybrid model underwent rigorous training and validation using the BraTS2021 datasets, followed by testing with the UPenn-GBM datasets. Moreover, the study conducted a comparative analysis of the proposed model's efficacy with different combinations of inputs using the UPenn-GBM dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe segmentation accuracy of residual U-Net on the full dataset was evaluated on DICE values of 85.62 (ET), 89.96 (TC), and 92.01 (WT) on the BraTS2021 dataset. Under a limited \u0026nbsp;T2w and FLAIR \u0026nbsp;dataset, the performance was 90.22 (ET), 91.77 (TC), and 92.78 (WT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThis study proposes a model for brain tumor segmentation. An experiment conducted on comparable and challenging datasets demonstrated better performance using both the full dataset and limited data available.\u003c/p\u003e","manuscriptTitle":"Cascaded Hybrid Models for Brain Tumor MRI Segmentation under limited dataset conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 11:02:02","doi":"10.21203/rs.3.rs-6455496/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-26T12:35:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68232887597349708421052594632254217588","date":"2025-08-21T12:11:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97259203453287776599980548230780001387","date":"2025-08-14T09:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T09:40:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T09:38:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-17T06:32:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-17T00:54:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-06-17T00:50:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a6756be-d779-4de5-b5bb-b5dc51a5cb1f","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-22T11:02:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 11:02:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6455496","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6455496","identity":"rs-6455496","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}