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Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads todissection or rupture through progressive enlargement of the aorta. It isusually asymptomatic and screening recommendation are limited. Thegold-standard evaluation is performed by computed tomography angiography (CTA)and radiologists time-consuming assessment. Scans for other indications couldhelp on this screening, however if acquired without contrast enhancement orwith low dose protocol, it can make the clinical evaluation difficult, besidesincreasing the scans quantity for the radiologists. In this study, it wasselected 587 unique CT scans including control and TAA patients, acquired withlow and standard dose protocols, with or without contrast enhancement. A novelsegmentation model, DeepVox, exhibited dice score coefficients of 0.932 and0.897 for development and test sets, respectively, with faster training speedin comparison to models reported in the literature. The novel TAAclassification model, SAVE-CT, presented accuracies of 0.930 and 0.922 fordevelopment and test sets, respectively, using only the binary segmentationmask from DeepVox as input, without hand-engineered features. These two modelstogether are a potential approach for TAA screening, as they can handlevariable number of slices as input, handling thoracic and thoracoabdominalsequences, in a fully automated contrast- and dose-independent evaluation. Thismay assist to decrease TAA mortality and prioritize the evaluation queue ofpatients for radiologists.

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