DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning
  approach for thoracic aorta segmentation and aneurysm prediction using
  computed tomography scans
Matheus del-Valle Lariza Laura de Oliveira Henrique Cursino Vieira Henrique Min Ho Lee Lucas Lembrança Pinheiro Maria Fernanda Portugal Newton Shydeo Brandão Miyoshi Nelson Wolosker
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.