  This study evaluates two approaches applied to computed tomography (CT)
images of patients with abdominal aortic aneurysm: one deterministic, based on
tools of Approximation Theory, and one based on Artificial Intelligence. Both
aim to segment the basal CT images to extract the patent area of the aortic
vessel, in order to propose an alternative to nephrotoxic contrast agents for
diagnosing this pathology. While the deterministic approach employs sampling
Kantorovich operators and the theory behind, leveraging the reconstruction and
enhancement capabilities of these operators applied to images, the artificial
intelligence-based approach lays on a U-net neural network. The results
obtained from testing the two methods have been compared numerically and
visually to assess their performances, demonstrating that both models yield
accurate results.
