Topological Shape Representation for Aneurysm -- Bifurcation Detection
2026-07-06 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors address the problem of computers mistakenly confusing small brain aneurysms with normal blood vessel branches in CT scans. They introduce a new method called SECT that looks at the overall 3D shape of blood vessels instead of just pixel brightness. This method is much better at correctly identifying tiny aneurysms, even across different scanner machines. Their approach could help reduce false alarms and improve automated diagnosis of brain aneurysms.
Intracranial aneurysmsCT angiographyConvolutional neural networksFalse-positive reductionSmooth Euler Characteristic TransformTopological data analysisPersistence imagesVascular bifurcationAUC (Area Under Curve)Scanner-agnostic
Authors
Akshay Gokhale, Mansi Dhamne
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 (<3 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.