Automated Mandibular Canal Segmentation on CBCT using Deep Learning

Automated Mandibular Canal Segmentation on CBCT 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 Automated Mandibular Canal Segmentation on CBCT using Deep Learning Jingna Huang, Ji Jie, Huibin Ma, Shimin Xie, Huangan Liao, Kexiong Ouyang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6969939/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2025 Read the published version in BMC Oral Health → Version 1 posted 14 You are reading this latest preprint version Abstract Objective: This study aims to develop a publicly accessible dataset for mandibular canal segmentation in cone beam computed tomography (CBCT) scans and to propose a framework for automated mandibular canal segmentation. Methods: A total of 236 CBCT scans were collected from the Stomatology Hospital of the Shantou University Medical College, and the mandibular canals in these files were finely annotated. A custom designed 3D UNet, named MyResUNet, along with two commonly used UNet models, were used as candidate models. Soft dice similarity coefficient (DSC) loss was used as the loss function. A post-processing step involving connected components analysis and removing small objects was applied during inference. Model performance was assessed using voxel accuracy (ACC), sensitivity (SEN), specificity (SPE),DSC(1), Hausdorff distance (HD), the 95th percentile Hausdorff distance (HD95), average surface distance (ASD), and average symmetric surface distance (ASSD). Results: The MCSTU dataset, which contains a development dataset and an independent test dataset comprising 218 and 18 CBCT images with fine-grained annotations, respectively, has been made publicly available. The validation loss of MyResUNet was lower than that of two commonly used models. The inclusion of post-processing significantly enhanced the performance, especially by reducing the HD metric. On the hold-out test dataset, the MyResUNet model achieved ACC, SEN, SPE, DSC, HD, HD95, ASD, ASSD with 95% confidence interval of 1 (1-1), 0.86 (0.83-0.87), 1 (1-1), 0.85 (0.83-0.86), 10.1 (8.67-13.6), 1.8 (1.6-2.2), 0.69 (0.58-0.85), and 0.72 (0.6-0.83), respectively. On the test dataset, the MyResUNet model obtained ACC, SEN, SPE, DICE, HD, HD95, ASD, ASSD(2, 3)with 95% confidence interval of 1 (1-1), 0.93 (0.91-0.95), 1 (1-1), 0.80 (0.79-0.81), 21.3 (11.7-53.9), 2.59 (2.33-3), 1 (0.96-1.21), and 0.92 (0.861-1), respectively. Both the code and trained models are publicly available. Conclusion: The proposed segmentation framework achieved strong performance on both the hold-out and independent test datasets. In the future, after further validation of the model’s generalization ability, it may be applied in real clinical settings for oral surgery planning. Mandibular canal segmentation CBCT 3D UNet ResUNet Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2025 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 26 Jul, 2025 Reviews received at journal 26 Jul, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 20 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor invited by journal 10 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 24 Jun, 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. 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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-6969939","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484760314,"identity":"debe90f1-951d-48a5-85ec-2127141ac4b4","order_by":0,"name":"Jingna Huang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingna","middleName":"","lastName":"Huang","suffix":""},{"id":484760315,"identity":"1b240b10-aba7-4d3f-8e74-e24404afa56b","order_by":1,"name":"Ji Jie","email":"","orcid":"","institution":"Network \u0026 Information Center of Shantou 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