  Cardiac MR image segmentation is essential for the morphological and
functional analysis of the heart. Inspired by how experienced clinicians assess
the cardiac morphology and function across multiple standard views (i.e. long-
and short-axis views), we propose a novel approach which learns anatomical
shape priors across different 2D standard views and leverages these priors to
segment the left ventricular (LV) myocardium from short-axis MR image stacks.
The proposed segmentation method has the advantage of being a 2D network but at
the same time incorporates spatial context from multiple, complementary views
that span a 3D space. Our method achieves accurate and robust segmentation of
the myocardium across different short-axis slices (from apex to base),
outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher
data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean
Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to
2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test
set, when only 10% of the training data was used.
