Bayesian Aneurysm Growth Detection via Surface Displacement Modeling

TL;DR

This paper introduces a Bayesian surface displacement model for detecting intracranial aneurysm growth from longitudinal MRA scans, improving accuracy and interpretability over traditional methods.

Contribution

The authors develop a probabilistic framework that leverages vessel surface registration and displacement analysis, providing calibrated growth probabilities and robustness to label variability.

Findings

Achieves high discrimination (AUC 0.86-0.87) in aneurysm growth detection.

Improves agreement with expert labels (Cohen's kappa up to 0.66).

Provides interpretable uncertainty bounds for clinical decision-making.

Abstract

Clinical decisions for unruptured intracranial aneurysms depend on detecting growth on follow-up magnetic resonance angiography (MRA). Growth is typically judged from manual 2D diameters on few slices, which vary across clinicians and frequently miss subtle 3D change. Even with 3D segmentations, apparent differences can reflect resolution, segmentation, surface processing, or registration mismatch rather than true growth; most criteria remain heuristic and binary. We show that a Bayesian displacement-based model using the surrounding vessel as an internal reference achieves strong discrimination of aneurysm growth (AUC 0.86-0.87) and improves agreement with expert labels (Cohen's kappa up to 0.66 vs. 0.35 for volumetric criteria), while providing calibrated posterior probabilities with uncertainty bounds. The method registers baseline and follow-up surfaces, computes normal-directed displacements, and summarizes change as the difference between mean aneurysm displacement and mean displacement on the surrounding non-aneurysmal vessel segment. The vessel segment serves as an internal control for imaging and processing variability, assuming negligible structural change over the surveillance interval. We evaluate two cohorts spanning time-of-flight and contrast-enhanced longitudinal MRA studies: a public dataset labeled from neuroradiologist-provided measurements and an institutional dataset labeled by senior and junior raters. Performance is preserved when training on lower-expertise labels, indicating robustness to label variability. Calibrated probabilities may aid clinical decision-making in borderline cases, where high uncertainty can motivate repeat imaging. This framework provides interpretable probabilistic growth assessment from longitudinal MRA, reduces dependence on clinician expertise, and supports cross-center surveillance across scanners and angiography sequences.