Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to
dissection or rupture through progressive enlargement of the aorta. It is
usually asymptomatic and screening recommendation are limited. The
gold-standard evaluation is performed by computed tomography angiography (CTA)
and radiologists time-consuming assessment. Scans for other indications could
help on this screening, however if acquired without contrast enhancement or
with low dose protocol, it can make the clinical evaluation difficult, besides
increasing the scans quantity for the radiologists. In this study, it was
selected 587 unique CT scans including control and TAA patients, acquired with
low and standard dose protocols, with or without contrast enhancement. A novel
segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and
0.897 for development and test sets, respectively, with faster training speed
in comparison to models reported in the literature. The novel TAA
classification model, SAVE-CT, presented accuracies of 0.930 and 0.922 for
development and test sets, respectively, using only the binary segmentation
mask from DeepVox as input, without hand-engineered features. These two models
together are a potential approach for TAA screening, as they can handle
variable number of slices as input, handling thoracic and thoracoabdominal
sequences, in a fully automated contrast- and dose-independent evaluation. This
may assist to decrease TAA mortality and prioritize the evaluation queue of
patients for radiologists.

这项研究探索了使用低剂量 CT 扫描和无对比剂加强 CT 扫描获得的图像进行自动化筛查、评估和分类的方法，开发了一种新型分割模型和 TAA 分类模型，可在不同条件下进行全自动的无对比剂和剂量独立的 TAA 评估，以减少患者的筛查时间和病理评估风险。

DeepVox 和 SAVE-CT：一种对比度和剂量无关的三维深度学习方法，用于胸部主动脉分割和动脉瘤预测的计算机断层扫描

DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning  approach for thoracic aorta segmentation and aneurysm prediction using  computed tomography scans