  In many medical image analysis applications, often only a limited amount of
training data is available, which makes training of convolutional neural
networks (CNNs) challenging. In this work on anatomical landmark localization,
we propose a CNN architecture that learns to split the localization task into
two simpler sub-problems, reducing the need for large training datasets. Our
fully convolutional SpatialConfiguration-Net (SCN) dedicates one component to
locally accurate but ambiguous candidate predictions, while the other component
improves robustness to ambiguities by incorporating the spatial configuration
of landmarks. In our experimental evaluation, we show that the proposed SCN
outperforms related methods in terms of landmark localization error on
size-limited datasets.
