  Incorporation of prior knowledge about organ shape and location is key to
improve performance of image analysis approaches. In particular, priors can be
useful in cases where images are corrupted and contain artefacts due to
limitations in image acquisition. The highly constrained nature of anatomical
objects can be well captured with learning based techniques. However, in most
recent and promising techniques such as CNN based segmentation it is not
obvious how to incorporate such prior knowledge. State-of-the-art methods
operate as pixel-wise classifiers where the training objectives do not
incorporate the structure and inter-dependencies of the output. To overcome
this limitation, we propose a generic training strategy that incorporates
anatomical prior knowledge into CNNs through a new regularisation model, which
is trained end-to-end. The new framework encourages models to follow the global
anatomical properties of the underlying anatomy (e.g. shape, label structure)
via learned non-linear representations of the shape. We show that the proposed
approach can be easily adapted to different analysis tasks (e.g. image
enhancement, segmentation) and improve the prediction accuracy of the
state-of-the-art models. The applicability of our approach is shown on
multi-modal cardiac datasets and public benchmarks. Additionally, we
demonstrate how the learned deep models of 3D shapes can be interpreted and
used as biomarkers for classification of cardiac pathologies.
