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.

本研究评估了应用于腹主动脉瘤患者计算机断层扫描图像的两种方法：一种基于近似理论工具的确定性方法和一种基于人工智能的方法。两种方法均旨在分割基底 CT 图像，以提取主动脉血管的患区，为诊断该病理提供尿毒症造影剂的替代方案。实验结果定量和定性地比较了这两种方法的性能，表明两种模型均能得出准确的结果。

腹主动脉瘤计算机断层扫描图像的近似方法与人工智能方法的比较

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

An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if
not treated, tends to grow and may rupture. A significant unmet need in the
assessment of AAA disease, for the diagnosis, prognosis and follow-up, is the
determination of rupture risk, which is currently based on the manual
measurement of the aneurysm diameter in a selected Computed Tomography
Angiography (CTA) scan. However, there is a lack of standardization determining
the degree and rate of disease progression, due to the lack of robust,
automated aneurysm segmentation tools that allow quantitatively analyzing the
AAA. In this work, we aim at proposing the first 3D convolutional neural
network for the segmentation of aneurysms both from preoperative and
postoperative CTA scans. We extensively validate its performance in terms of
diameter measurements, to test its applicability in the clinical practice, as
well as regarding the relative volume difference, and Dice and Jaccard scores.
The proposed method yields a mean diameter measurement error of 3.3 mm, a
relative volume difference of 8.58 %, and Dice and Jaccard scores of 87 % and
77 %, respectively. At a clinical level, an aneurysm enlargement of 10 mm is
considered relevant, thus, our method is suitable to automatically determine
the AAA diameter and opens up the opportunity for more complex aneurysm
analysis.

本文提出了首个用于分割腹主动脉瘤的 3D 卷积神经网络，并成功地验证了其在诊断和评估方面的性能，该方法可自动量化主动脉瘤直径，开拓更复杂的主动脉瘤分析机会。