  Cardiovascular diseases are among the leading causes of death globally.
Cardiac left ventricle (LV) quantification is known to be one of the most
important tasks for the identification and diagnosis of such pathologies. In
this paper, we propose a deep learning method that incorporates 3D
spatio-temporal convolutions to perform direct left ventricle quantification
from cardiac MR sequences. Instead of analysing slices independently, we
process stacks of temporally adjacent slices by means of 3D convolutional
kernels which fuse the spatio-temporal information, incorporating the temporal
dynamics of the heart to the learned model. We show that incorporating such
information by means of spatio-temporal convolutions into standard LV
quantification architectures improves the accuracy of the predictions when
compared with single-slice models, achieving competitive results for all
cardiac indices and significantly breaking the state of the art (Xue et al.,
2018, MedIA) for cardiac phase estimation.
