{"ID":5936955,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T16:45:10.440590912Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05317","arxiv_id":"2607.05317","title":"Shape Over Intensity: Directional Topological Encoding for False Positive Reduction in Intracranial Aneurysm Detection","abstract":"Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural networks (CNNs) rely on local pixel intensities, causing systematic confusion between saccular aneurysms and vascular bifurcations -- a problem especially acute for small lesions (\u003c3 mm), where detection sensitivity falls below 60%. We propose a plug-and-play, topology-aware false-positive reduction framework evaluating the Smooth Euler Characteristic Transform (SECT) -- a directional representation encoding global 3D vascular geometry independently of intensity -- against persistence-based summaries (Persistence Images and Landscapes), tested on a stratified subset of the RSNA 2025 dataset. SECT achieves an AUC of 0.943, substantially outperforming direction-agnostic methods (AUC ~0.68), and exhibits a clinical performance inversion: it excels on the sub-3 mm cohort, maintaining 0.943 AUC and 78.5% sensitivity at 95% specificity. The representation is also scanner-agnostic, achieving 0.927 mean AUC under leave-one-scanner-out (LOGO) validation across four manufacturers. By capturing asymmetric geometric invariants rather than intensity profiles, SECT reliably resolves the primary structural confounder in IA detection, positioning it as a robust downstream filter for hybrid deep-learning diagnostic pipelines.","short_abstract":"Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural networks (CNNs) rely on local pixel intensities, causing systematic confusion between saccular aneurysms and vascular bifurcations -- a problem especially acute for small...","url_abs":"https://arxiv.org/abs/2607.05317","url_pdf":"https://arxiv.org/pdf/2607.05317v1","authors":"[\"Akshay Gokhale\",\"Mansi Dhamne\"]","published":"2026-07-06T16:56:17Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}