{"paper_id":"08a1c3d4-25d2-48db-a87e-1a2d779d11c4","body_text":"Detection and Segmentation of Pulmonary Embolism in 3D CT Pulmonary Angiography Using a Threshold Adjustment Segmentation Network | 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 Detection and Segmentation of Pulmonary Embolism in 3D CT Pulmonary Angiography Using a Threshold Adjustment Segmentation Network Jian-cong Fan, Haoyang luan, Yang Li, Yaqian qiao, Yande Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5298357/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Pulmonary embolism is a life-threatening condition where early diagnosis and precise localization are crucial for improving patient outcomes. While CT pulmonary angiography (CTPA) is the primary method for detecting pulmonary embolism, existing segmentation algorithms struggle to effectively distinguish thrombi from vascular structures in complex 3D CTPA images, often leading to both false positives and false negatives. To address these challenges, the Threshold Adjustment Segmentation Network (TSNet) is proposed to enhance segmentation performance in 3D CTPA images. TSNet incorporates two core modules: the Threshold Adjustment Module (TAD) and the Geometric-Topological Axial Feature Module (GT-AFM). TAD utilizes logarithmic scaling, adaptive adjustments, and nonlinear transformations to optimize the probability distributions of thrombi and vessels, reducing false positives while improving the sensitivity of thrombus detection. GT-AFM integrates geometric features and topological information to enhance the recognition of complex vascular and thrombotic structures, improving spatial feature processing. Experimental results show that TSNet achieves a sensitivity of 0.761 and a false positives per scan of 1.273 at ε = 0 mm. With an increased tolerance of ε = 5 mm, sensitivity improves to 0.878 and false positives per scan decreases to 0.515, significantly reducing false positives. These results indicate that TSNet demonstrates superior segmentation performance under various tolerance levels, showing robustness and a well-balanced trade-off between sensitivity and false positives, making it highly promising for clinical applications. Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Biological techniques/Imaging Pulmonary embolism CT pulmonary angiography 3D image detection and segmentation Threshold adjustment Medical image processing Full Text Additional Declarations No competing interests reported. Supplementary Files MedicalEthicsCommitteeoftheAffiliatedHospitalofQingdaoUniversity.pdf Cite Share Download PDF Status: Published Journal Publication published 01 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Dec, 2024 Reviews received at journal 23 Dec, 2024 Reviews received at journal 17 Dec, 2024 Reviewers agreed at journal 14 Dec, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviewers invited by journal 13 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Editor invited by journal 08 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 20 Oct, 2024 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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While CT pulmonary angiography (CTPA) is the primary method for detecting pulmonary embolism, existing segmentation algorithms struggle to effectively distinguish thrombi from vascular structures in complex 3D CTPA images, often leading to both false positives and false negatives. To address these challenges, the Threshold Adjustment Segmentation Network (TSNet) is proposed to enhance segmentation performance in 3D CTPA images. TSNet incorporates two core modules: the Threshold Adjustment Module (TAD) and the Geometric-Topological Axial Feature Module (GT-AFM). TAD utilizes logarithmic scaling, adaptive adjustments, and nonlinear transformations to optimize the probability distributions of thrombi and vessels, reducing false positives while improving the sensitivity of thrombus detection. GT-AFM integrates geometric features and topological information to enhance the recognition of complex vascular and thrombotic structures, improving spatial feature processing. 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