  In this work we address the problem of landmark-based segmentation for
anatomical structures. We propose HybridGNet, an encoder-decoder neural
architecture which combines standard convolutions for image feature encoding,
with graph convolutional neural networks to decode plausible representations of
anatomical structures. We benchmark the proposed architecture considering other
standard landmark and pixel-based models for anatomical segmentation in chest
x-ray images, and found that HybridGNet is more robust to image occlusions. We
also show that it can be used to construct landmark-based segmentations from
pixel level annotations. Our experimental results suggest that HybridGNet
produces accurate and anatomically plausible landmark-based segmentations, by
naturally incorporating shape constraints within the decoding process via
spectral convolutions.
