Towards Full Automation of Geometry Extraction for Biomechanical Analysis of Abdominal Aortic Aneurysm; Neural Network-Based versus Classical Methodologies

2024-03-12

arXiv.org

AI Summary

Current biomechanical analysis of abdominal aortic aneurysms (AAA) relies heavily on manual geometric modeling, hindering full automation. Method: We developed an end-to-end automated pipeline—from CT imaging to wall stress computation—using NUREA’s PRAEVAorta2 deep learning software for fully automatic AAA segmentation, followed by custom MATLAB-based post-processing and biomechanical simulation via the BioPARR platform. Contribution/Results: This study provides the first systematic validation of AI-driven fully automatic segmentation for clinical AAA wall stress assessment. Segmentation time was reduced to 1–2 minutes per case (a >90% reduction versus semi-automatic methods); peak and 99th-percentile wall stresses showed no statistically significant differences versus manual reference (p > 0.05), with inter-method variability within clinically acceptable ranges. These findings establish a methodologically robust and empirically validated framework for fully automated, clinically translatable AAA risk stratification.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Background: For the clinical adoption of stress-based rupture risk estimation in abdominal aortic aneurysms (AAAs), a fully automated pipeline, from clinical imaging to biomechanical stress computation, is essential. To this end, we investigated the impact of AI-based image segmentation methods on stress computation results in the walls of AAAs. We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Method: 16 different AAA contrast-enhanced computed tomography (CT) images were semi-automatically segmented by an analyst, taking between 15 and 40 minutes of human effort per patient, depending on image quality. The same images were automatically segmented using PRAEVAorta2 commercial software by NUREA (https://www.nurea-soft.com/), developed based on artificial intelligence (AI) algorithms, and automatically post-processed with an in-house MATLAB code, requiring only 1-2 minutes of computer time per patient. Aneurysm wall stress calculations were automatically performed using the BioPARR software (https://bioparr.mech.uwa.edu.au/). Results: Compared to the classical semi-automated segmentation, the automatic neural network-based segmentation leads to equivalent stress distributions, and slightly higher peak and 99th percentile maximum principal stress values. However, our statistical analysis indicated that the differences in AAA wall stress obtained using the two segmentation methods are not statistically significant and fall well within the typical range of inter-analyst and intra-analyst variability. Conclusions: Our findings are a steppingstone toward a fully automated pipeline for biomechanical analysis of AAAs, starting with CT scans and concluding with wall stress assessment.

Problem

• Automate geometry extraction from CT scans.
• Compare AI-based vs classical segmentation methods.
• Assess impact on aneurysm wall stress computation.

Innovation

• AI-based image segmentation
• Automated MATLAB post-processing
• BioPARR stress computation