RFFA-Net: Recursive Filtering and Feature Aggregation Driven Network for Tooth Segmentation in CBCT image

RFFA-Net: Recursive Filtering and Feature Aggregation Driven Network for Tooth Segmentation in CBCT image | 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 RFFA-Net: Recursive Filtering and Feature Aggregation Driven Network for Tooth Segmentation in CBCT image yu ao, hongze han, yuqin li, yu miao, weili shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9263419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Automatic tooth segmentation has significantly improved the accuracy and efficiency of clinical diagnosis, while also providing a technical foundation for the development of patient-specific treatment strategies. Although significant progress has been made in automatic tooth segmentation research, existing methods still struggle to accurately segment tooth structures, when confronted with the presence of metal artefacts and blurred boundaries in dental CBCT images. To address these challenges, this paper proposes an encoder-decoder network (RFFA-Net) driven by recursive filtering and feature aggregation strategies for three-dimensional segmentation of tooth structures. First, a Dense Mamba (DM) module is introduced into the encoder of the proposed network, which combines recursive filtering and dense connections to enhance feature extraction capabilities and reduce the interference caused by metal artifacts in CBCT images. Second, a dynamic multi-scale feature fusion (DMFF) module is designed at the network bottleneck. This module adaptively aggregates multi-scale features extracted from the encoder to better capture the irregular shapes of teeth. Finally, to address the issue of blurred tooth boundaries in CBCT images, an Inter-Channel-Spatial Attention (ICSA) module is introduced. This module employs a gating mechanism to dynamically adjust the weights of both channel and spatial attention, thereby effectively fusing global semantic information and local details. We conducted experiments on two commonly used dental CBCT datasets. The results indicate that our method outperforms existing state-of-the-art tooth segmentation methods, demonstrating its effectiveness, robustness, and superiority. In particular, in the presence of metal artifacts and blurred boundaries commonly found in CBCT images, the proposed method still maintains stable and accurate segmentation performance. CBCT images Deep learning Tooth segmentation Vision Mamba Feature Aggregation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 30 Mar, 2026 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-9263419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621628197,"identity":"9fd1f611-dd26-4861-8eb5-8e94c32a9c39","order_by":0,"name":"yu ao","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"yu","middleName":"","lastName":"ao","suffix":""},{"id":621628201,"identity":"5d86cc42-330c-446e-be2f-64f91cd6336a","order_by":1,"name":"hongze han","email":"","orcid":"","institution":"Changchun University of Science and 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