  Deformable registration continues to be one of the key challenges in medical
image analysis. While iconic registration methods have started to benefit from
the recent advances in medical deep learning, the same does not yet apply for
the registration of point sets, e.g. registration based on surfaces, keypoints
or landmarks. This is mainly due to the restriction of the convolution operator
in modern CNNs to densely gridded input. However, with the newly developed
methods from the field of geometric deep learning suitable tools are now
emerging, which enable powerful analysis of medical data on irregular domains.
In this work, we present a new method that enables the learning of regularized
feature descriptors with dynamic graph CNNs. By incorporating the learned
geometric features as prior probabilities into the well-established coherent
point drift (CPD) algorithm, formulated as differentiable network layer, we
establish an end-to-end framework for robust registration of two point sets.
Our approach is evaluated on the challenging task of aligning keypoints
extracted from lung CT scans in inhale and exhale states with large
deformations and without any additional intensity information. Our results
indicate that the inherent geometric structure of the extracted keypoints is
sufficient to establish descriptive point features, which yield a significantly
improved performance and robustness of our registration framework.
