  Morphological analysis and identification of pathologies in the aorta are
important for cardiovascular diagnosis and risk assessment in patients. Manual
annotation is time-consuming and cumbersome in CT scans acquired without
contrast enhancement and with low radiation dose. Hence, we propose an
automatic method to segment the ascending aorta, the aortic arch and the
thoracic descending aorta in low-dose chest CT without contrast enhancement.
Segmentation was performed using a dilated convolutional neural network (CNN),
with a receptive field of 131X131 voxels, that classified voxels in axial,
coronal and sagittal image slices. To obtain a final segmentation, the obtained
probabilities of the three planes were averaged per class, and voxels were
subsequently assigned to the class with the highest class probability. Two-fold
cross-validation experiments were performed where ten scans were used to train
the network and another ten to evaluate the performance. Dice coefficients of
0.83, 0.86 and 0.88, and Average Symmetrical Surface Distances (ASSDs) of 2.44,
1.56 and 1.87 mm were obtained for the ascending aorta, the aortic arch, and
the descending aorta, respectively. The results indicate that the proposed
method could be used in large-scale studies analyzing the anatomical location
of pathology and morphology of the thoracic aorta.
