Judy Shum

TL;DR: It is hypothesized that aneurysm morphology and wall thickness are more predictive of rupture risk and can be the deciding factors in the clinical management of the disease.

TL;DR: Biomechanical factors identified by means of computational modeling techniques, such as peak wall stress, have been positively correlated with rupture risk with a higher accuracy and sensitivity than maximum diameter alone.

TL;DR: While further refinement is needed to fully automate the outer wall segmentation algorithm, these preliminary results demonstrate the method's adequate reproducibility and low interobserver variability.

TL;DR: A discriminatory analysis of aneurysm geometry characterization revealed that the L2-norm of the Gaussian and Mean surface curvatures can be utilized as classifiers of the three AAA population subgroups, and a combination of non-invasive geometric quantification and statistical machine learning methods can be used in a clinical setting to assess the risk of rupture ofaneurysms during regular patient follow-ups.

TL;DR: Patient-specific abdominal aortic aneurysms (AAAs) are characterized by local curvature changes, which are assessed using a feature-based approach on topologies representative of the AAA outer wall surface using a Delaunay triangulation algorithm adapted for AAA segmented masks.