An approximation-based approach versus an AI one for the study of CT images of abdominal aorta aneurysms

TL;DR

This paper compares a deterministic approximation method and an AI-based neural network approach for segmenting CT images of abdominal aortic aneurysms, aiming to reduce reliance on nephrotoxic contrast agents.

Contribution

It introduces and evaluates an approximation theory-based segmentation method alongside a U-net neural network for aneurysm imaging analysis.

Findings

Both methods produce accurate segmentation results.

The approximation approach offers a viable alternative to AI.

The methods are comparable in performance.

Abstract

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.