  Fully automatic cardiac segmentation can be a fast and reproducible method to
extract clinical measurements from an echocardiography examination. The U-Net
architecture is the current state-of-the-art deep learning architecture for
medical segmentation and can segment cardiac structures in real-time with
average errors comparable to inter-observer variability. However, this
architecture still generates large outliers that are often anatomically
incorrect. This work uses the concept of graph convolutional neural networks
that predict the contour points of the structures of interest instead of
labeling each pixel. We propose a graph architecture that uses two
convolutional rings based on cardiac anatomy and show that this eliminates
anatomical incorrect multi-structure segmentations on the publicly available
CAMUS dataset. Additionally, this work contributes with an ablation study on
the graph convolutional architecture and an evaluation of clinical measurements
on the clinical HUNT4 dataset. Finally, we propose to use the inter-model
agreement of the U-Net and the graph network as a predictor of both the input
and segmentation quality. We show this predictor can detect out-of-distribution
and unsuitable input images in real-time. Source code is available online:
https://github.com/gillesvntnu/GCN_multistructure
