  We aim to reduce the tedious nature of developing and evaluating methods for
aligning PET-CT scans from multiple patient visits. Current methods for
registration rely on correspondences that are created manually by medical
experts with 3D manipulation, or assisted alignments done by utilizing mutual
information across CT scans that may not be consistent when transferred to the
PET images. Instead, we propose to label multiple key points across several 2D
slices, which we then fit a key curve to. This removes the need for creating
manual alignments in 3D and makes the labelling process easier. We use these
key curves to define an error metric for the alignments that can be computed
efficiently. While our metric is non-differentiable, we further show that we
can utilize it during the training of our deep model via a novel method.
Specifically, instead of relying on detailed geometric labels -- e.g., manual
3D alignments -- we use synthetically generated deformations of real data. To
incorporate robustness to changes that occur between visits other than
geometric changes, we enforce consistency across visits in the deep network's
internal representations. We demonstrate the potential of our method via
qualitative and quantitative experiments.
