  We propose a method based on deep learning to perform cardiac segmentation on
short axis MRI image stacks iteratively from the top slice (around the base) to
the bottom slice (around the apex). At each iteration, a novel variant of U-net
is applied to propagate the segmentation of a slice to the adjacent slice below
it. In other words, the prediction of a segmentation of a slice is dependent
upon the already existing segmentation of an adjacent slice. 3D-consistency is
hence explicitly enforced. The method is trained on a large database of 3078
cases from UK Biobank. It is then tested on 756 different cases from UK Biobank
and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with
30 cases, RVSC with 16 cases). Results comparable or even better than the
state-of-the-art in terms of distance measures are achieved. They also
emphasize the assets of our method, namely enhanced spatial consistency
(currently neither considered nor achieved by the state-of-the-art), and the
generalization ability to unseen cases even from other databases.
