Evaluation of the image quality index with MRI motion artifacts on tumor segmentation using deep learning

Evaluation of the image quality index with MRI motion artifacts on tumor segmentation using deep learning | 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 Evaluation of the image quality index with MRI motion artifacts on tumor segmentation using deep learning Masafumi Akanuma, Keisuke Usui, Ryoma Tsuchiya, Eiichi Maehara, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8098211/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In magnetic resonance imaging (MRI), motion artifacts are a major factor that degrades image quality and diagnostic accuracy. Although deep learning-based high-precision segmentation techniques have emerged of late, the impact of image quality degradation on segmentation accuracy is not sufficiently understood, and clinically acceptable image quality standards are not yet clearly defined. This study aims to quantitatively evaluate the effects of motion artifacts on brain-tumor segmentation using deep learning and clarify clinically acceptable image quality criteria. Fluid-attenuated inversion recovery ( FLAIR ) images of glioma patients were used to manually delineate tumor contours, and a segmentation model was constructed using an AI development support service based on the contour data. Simulated motion artifact images were generated, and the relationship between segmentation accuracy and image quality was analyzed. Segmentation accuracy was evaluated using the dice similarity coefficient (DSC), while image quality was assessed using mean absolute error, root mean squared error, structural similarity index measure (SSIM), and peak signal-to-noise ratio. No strong correlation was observed between DSC and the four image quality indices; however, SSIM differed significantly between cases with DSC ≥ 0.8 and DSC < 0.8. Furthermore, visual evaluation by three experienced radiological technologists revealed a positive correlation between SSIM and perceived image quality; when an SSIM of 0.8 was used as a threshold, the visual evaluations differed significantly. These results suggest that SSIM is an effective indicator of human visual perception of image quality, and an SSIM threshold of 0.8 may represent a clinically acceptable standard for MRI image quality. Motion artifacts Glioblastoma MRI Deep learning Dice similarity coefficient Structural similarity index measure Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8098211","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556913286,"identity":"9b6220a8-c0de-4128-bebf-305529190403","order_by":0,"name":"Masafumi Akanuma","email":"","orcid":"","institution":"Juntendo University","correspondingAuthor":false,"prefix":"","firstName":"Masafumi","middleName":"","lastName":"Akanuma","suffix":""},{"id":556913287,"identity":"0ada55d1-56c9-41ad-8dd3-efafdb3d09ae","order_by":1,"name":"Keisuke 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learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Motion artifacts, Glioblastoma, MRI, Deep learning, Dice similarity coefficient, Structural similarity index measure","lastPublishedDoi":"10.21203/rs.3.rs-8098211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8098211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn magnetic resonance imaging (MRI), motion artifacts are a major factor that degrades image quality and diagnostic accuracy. Although deep learning-based high-precision segmentation techniques have emerged of late, the impact of image quality degradation on segmentation accuracy is not sufficiently understood, and clinically acceptable image quality standards are not yet clearly defined. This study aims to quantitatively evaluate the effects of motion artifacts on brain-tumor segmentation using deep learning and clarify clinically acceptable image quality criteria. Fluid-attenuated inversion recovery (\u003cem\u003eFLAIR\u003c/em\u003e) images of glioma patients were used to manually delineate tumor contours, and a segmentation model was constructed using an AI development support service based on the contour data. Simulated motion artifact images were generated, and the relationship between segmentation accuracy and image quality was analyzed. Segmentation accuracy was evaluated using the dice similarity coefficient (DSC), while image quality was assessed using mean absolute error, root mean squared error, structural similarity index measure (SSIM), and peak signal-to-noise ratio. No strong correlation was observed between DSC and the four image quality indices; however, SSIM differed significantly between cases with DSC\u0026thinsp;\u0026ge;\u0026thinsp;0.8 and DSC\u0026thinsp;\u0026lt;\u0026thinsp;0.8. Furthermore, visual evaluation by three experienced radiological technologists revealed a positive correlation between SSIM and perceived image quality; when an SSIM of 0.8 was used as a threshold, the visual evaluations differed significantly. These results suggest that SSIM is an effective indicator of human visual perception of image quality, and an SSIM threshold of 0.8 may represent a clinically acceptable standard for MRI image quality.\u003c/p\u003e","manuscriptTitle":"Evaluation of the image quality index with MRI motion artifacts on tumor segmentation using deep learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 16:46:33","doi":"10.21203/rs.3.rs-8098211/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1859f445-f921-4078-b3ba-1b355c01ff6b","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-01T03:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 16:46:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8098211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8098211","identity":"rs-8098211","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}