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。