  Recent publications have shown that the segmentation accuracy of modern-day
convolutional neural networks (CNN) applied on cardiac MRI can reach the
inter-expert variability, a great achievement in this area of research.
However, despite these successes, CNNs still produce anatomically inaccurate
segmentations as they provide no guarantee on the anatomical plausibility of
their outcome, even when using a shape prior. In this paper, we propose a
cardiac MRI segmentation method which always produces anatomically plausible
results. At the core of the method is an adversarial variational autoencoder
(aVAE) whose latent space encodes a smooth manifold on which lies a large
spectrum of valid cardiac shapes. This aVAE is used to automatically warp
anatomically inaccurate cardiac shapes towards a close but correct shape. Our
method can accommodate any cardiac segmentation method and convert its
anatomically implausible results to plausible ones without affecting its
overall geometric and clinical metrics. With our method, CNNs can now produce
results that are both within the inter-expert variability and always
anatomically plausible.
