Uniform parametric mapping of saccular intracranial aneurysms for statistical analysis of morphological variation
Year: 2025
Type: article
Abstract: The morphology of saccular intracranial aneurysms is widely assumed to encode biomechanical information critical for rupture risk, yet existing predictors rely almost exclusively on handcrafted shape descriptors that are subjective and capture only limited aspects of the geometry. We introduce a data-driven framework for uniform parametric mapping and statistical shape analysis of aneurysm surfaces, applied to a multi-institutional set of 958 patient-extracted intracranial aneurysms. We develop a uniform parametric mapping based on an angular- and radial-field parameterization, yielding a surface map into a registered canonical space. The mapping is fully automatic, requires no manual annotations, and is independent of the cut configuration, meshing regularity, and sampling density. Using the mapping, we create uniform point correspondences across all aneurysms. From these correspondences, high-dimensional shape vectors are constructed, and principal component analysis (PCA) yields the mean aneurysm shape and dominant modes of variation. We investigate how strongly each mode contributes to the rupture association using logistic regression of the PCA coefficients. We identified three modes significantly associated with rupture status ( ) and provide interpretable deformation patterns for their morphological characteristics. The shape-based regression model classifying rupture status achieves an average accuracy of and an AUC of after 5-fold cross-validation. Based on a diverse range of aneurysm models, our framework demonstrates robust parameter mapping and statistical measures, offering a generalized, reproducible approach for shape-based risk stratification that may inform evidence-based management of unruptured aneurysms.
Source: Computers & Graphics
Authors: Pepe Eulzer, Kai Lawonn
Institution: Friedrich Schiller University Jena
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,770
OpenAlex ID: W4413780954