MyoRegenTrack: Quantifying the Recovery Process of Skeletal Muscle on HE-Stained Images via Learning from Label Proportion

MyoRegenTrack: Quantifying the Recovery Process of Skeletal Muscle on HE-Stained Images via Learning from Label Proportion | 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 Article MyoRegenTrack: Quantifying the Recovery Process of Skeletal Muscle on HE-Stained Images via Learning from Label Proportion Yu Yamaoka, Shigeto Seno, Weng Ian Chan, Kanako Iwamori, Soichiro Fukada, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4716323/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Visual observing muscle tissue regeneration is used to measure experimental effect size in biological research to discover the mechanism of muscle strength decline due to illness or aging. Quantitative computer imaging analysis for support evaluating the recovery stage has not been established because of the localized nature of recovery and the difficulty in selecting image features for cells in recovery stages. We constructed MyoRegenTrack for segmenting cells and classifying their regeneration stage in hematoxylin-eosin (HE) stained images. A straightforward approach to classification is supervised learning. However, obtaining detailed annotations for each fiber in a whole slide image is impractical regarding cost and accuracy. Thus, we propose to learn individual fiber stage classification utilizing the proportions of fiber stages depending on the days after muscle injection to induce regeneration. We extract implicit multidimensional features from the HE-stained tissue images and train a classifier using weakly supervised learning, guided by their class proportion for elapsed time on recovery. We confirmed the effectiveness of MyoRegenTrack by comparing its results with expert annotations. A comparative study of the recovery relation between two different muscle injections shows that the analysis result using MyoRegenTrack is consistent with findings from previous studies. Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Computer science Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Stem cells/Muscle stem cells Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Aug, 2024 Reviews received at journal 13 Aug, 2024 Reviews received at journal 07 Aug, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 01 Aug, 2024 Reviewers agreed at journal 01 Aug, 2024 Reviewers agreed at journal 01 Aug, 2024 Reviewers invited by journal 01 Aug, 2024 Editor assigned by journal 01 Aug, 2024 Editor invited by journal 15 Jul, 2024 Submission checks completed at journal 11 Jul, 2024 First submitted to journal 10 Jul, 2024 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-4716323","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":337385733,"identity":"86cec02f-f2f8-440f-88f0-c73e65d2cac2","order_by":0,"name":"Yu Yamaoka","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Yamaoka","suffix":""},{"id":337385734,"identity":"47264b64-a7e7-44d2-9318-9407368050fe","order_by":1,"name":"Shigeto 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