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

Cardiac MRI Segmentation with Strong Anatomical Guarantees