UNet for automatic pneumothorax detection on canine and feline CTs

UNet for automatic pneumothorax detection on canine and feline CTs | 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 UNet for automatic pneumothorax detection on canine and feline CTs Tommaso Banzato, Artur Jurgas, Tommaso Pilla, Diane Wilson, Marek Wodzinski, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8742540/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Pneumothorax is defined as the pathological presence of air or gas in the pleural space. This can be due to penetration of airthrough a pleuro-cutaneous, pleuro-pulmonary or pleuro-esophageal pathway. Pneumothorax is a potentially life-threateningcondition and its detection in routine clinical and emergency settings can be decisive for patient survival. Computed tomography(CT) plays a key role in determining its presence and extent. This retrospective study aimed to develop a deep learning-basedalgorithm for automatic segmentation of pneumothorax in dogs and cats. Data were collected from different facilities. Thepathological air accumulation was then manually segmented by experienced radiologists to create a ground truth. An nnU-Netframework was used to build the algorithm. One hundred cases were collected, and the model was trained on 80 cases andtested on the remaining 20. The model was then tested on 47 negative cases. Performances were evaluated using Dicesimilarity score (DSC), the Aggregated Dice Score Similarity Coefficient (DSCAgg), and the Average Symmetric SurfaceDistance (ASSD). The model reached a good detection capability with a DSC of 0.797, a DSCAgg of 0.9267 and an ASSDof 3.281. This study is the first reporting the development of a deep learning-based algorithm for automatic segmentation ofpneumothorax in dogs and cats on CT scans, suggesting the potential impact in clinical and emergency scenarios. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 30 Jan, 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. 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-8742540","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601411755,"identity":"dbf0b058-004d-48d4-b9ba-4739bbae3459","order_by":0,"name":"Tommaso 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