  In this study we investigated the impact of image segmentation methods on the
results of stress computation in the wall of abdominal aortic aneurysms (AAAs).
We compared wall stress distributions and magnitudes calculated from geometry
models obtained from classical semi-automated segmentation versus automated
neural network-based segmentation. Ten different AAA contrast-enhanced computed
tomography (CT) images were semi-automatically segmented by an analyst, taking,
depending on the quality of an image, between 15 and 40 minutes of human effort
per patient. The same images were automatically segmented using PRAEVAorta 2,
commercial software by NUREA (https://www.nurea-soft.com/), developed based on
artificial intelligence (AI) algorithms, requiring only 1-2 minutes of computer
time per patient. Aneurysm wall stress calculations performed using the BioPARR
software (https://bioparr.mech.uwa.edu.au/) revealed that, 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. This difference is due to
consistently larger lumen surface areas in automatically segmented models as
compared to classical semi-automated segmentations, resulting in greater total
pressure load on the wall. 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, while at the same time highlighting
the critical importance of the repeatable and accurate segmentation of the
lumen, the difficult problem often underestimated by the literature.
