Automated error localisation and correction techniques for deep- learning-based segmentation of 3D MRI sequences based on feature- derived-region aggregation

Automated error localisation and correction techniques for deep- learning-based segmentation of 3D MRI sequences based on feature- derived-region aggregation | 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 Automated error localisation and correction techniques for deep- learning-based segmentation of 3D MRI sequences based on feature- derived-region aggregation Adrian C. Ruckli, Valentin Roesler, Hanspeter Hess, Malin K. Meier, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7593191/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Automatic segmentation using convolutional neural networks (CNNs) has become a key tool in musculoskeletal imaging, offering substantial reductions in processing time. However, concerns about reliability often necessitate manual inspection and correction. We present a method that leverages network-derived uncertainty to automatically identify and localise segmentation errors, reducing the need for exhaustive manual review. A 3D nnU-Net was trained on delayed gadolinium-enhanced MRI of hip cartilage. Voxel-wise uncertainty scores, computed from the SoftMax outputs of ensembled sub-networks, were aggregated over feature-based supervoxels. Each region was then evaluated for its potential impact on clinically relevant metrics, generating sensitivity scores. A logistic model combined these with uncertainty data to assign risk scores, prioritising areas most likely to affect segmentation accuracy. Using these risk scores, guided supervoxel correction of just 50 supervoxels reduced the mean absolute relative error by 2.1-fold. Guided manual correction within these regions achieved a 3.5-fold reduction, an approximate 62% supervoxel correction efficiency. Correcting the top 10 regions yielded up to 88% efficiency. This approach enables targeted, efficient correction, enhancing the clinical utility of CNN-based segmentation by focusing effort where it matters most, and outperforming traditional 2D correction methods in speed and accuracy. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Deep Learning Automatic Segmentation Uncertainty Estimation Error Localisation Clinical Metric 3D Hip MRI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 26 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 13 Oct, 2025 Editor invited by journal 06 Oct, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 03 Oct, 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. 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-7593191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":540307254,"identity":"76c02832-dbec-4d60-9513-76db8995c16e","order_by":0,"name":"Adrian C. 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