Evaluation and mitigation of domain shift impact between volumetric submicro-scale and micro-scale computed tomography systems in the context of automated binary wood classification

Evaluation and mitigation of domain shift impact between volumetric submicro-scale and micro-scale computed tomography systems in the context of automated binary wood classification | 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 and mitigation of domain shift impact between volumetric submicro-scale and micro-scale computed tomography systems in the context of automated binary wood classification Jannik Stebani, Kilian Dremel, Tim Lewandrowski, Simon Zabler, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6667254/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 10 You are reading this latest preprint version Abstract Rapid and reliable automated identification of wood species can be a boon for applications across wood scientific context including forestry and biodiversity conservation, as well as in an industrial context via requirements for timber trade regulations. However, robust machine learning classifiers must be properly analyzed and immunized against domain shift effects. These can degrade the automated system performance for input data variations occurring in many scenarios. This work analyses the domain shift generated by using two differing sub-micro-scale and micro-scale computed tomography setups in the context of deep learning based binary wood classification from volumetric image data. Further, we examine several mitigation strategies and propose data- and model-level intertwined strategies to effectively minimize the performance domain gap. Core elements of the strategy include the combined usage of phase-correction methods, low-pass pyramid representation of the data and model normalization and regularization approaches. Vanishing domain performance differences led to the conclusion that the combined strategy ultimately prompted the model to learn robust features. These features are discriminative for input data from both sub-micro-system and micro-system domains, despite the substantial differences in data acquisition setup that propagate into fundamental image quality metrics like resolution, contrast and signal-to-noise ratio. Computed tomography machine learning automated wood identification deep learning domain gap binary classification Full Text Additional Declarations No competing interests reported. Supplementary information is not available with this version Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 27 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviews received at journal 28 May, 2025 Reviewers agreed at journal 24 May, 2025 Reviewers invited by journal 19 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 14 May, 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. 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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-6667254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458945145,"identity":"2bd2f935-0f5b-42d4-a234-a64bd419e458","order_by":0,"name":"Jannik 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