  Accurate and reproducible measurements of the aortic diameters are crucial
for the diagnosis of cardiovascular diseases and for therapeutic decision
making. Currently, these measurements are manually performed by healthcare
professionals, being time consuming, highly variable, and suffering from lack
of reproducibility. In this work we propose a supervised deep learning method
for the direct estimation of aortic diameters. The approach is devised and
tested over 100 magnetic resonance angiography scans without contrast agent.
All data was expert-annotated at six aortic locations typically used in
clinical practice. Our approach makes use of a 3D+2D convolutional neural
network (CNN) that takes as input a 3D scan and outputs the aortic diameter at
a given location. In a 5-fold cross-validation comparison against a fully 3D
CNN and against a 3D multiresolution CNN, our approach was consistently
superior in predicting the aortic diameters. Overall, the 3D+2D CNN achieved a
mean absolute error between 2.2-2.4 mm depending on the considered aortic
location. These errors are less than 1 mm higher than the inter-observer
variability. Thus, suggesting that our method makes predictions almost reaching
the expert's performance. We conclude that the work allows to further explore
automatic algorithms for direct estimation of anatomical structures without the
necessity of a segmentation step. It also opens possibilities for the
automation of cardiovascular measurements in clinical settings.
