  Medical image registration is a challenging task involving the estimation of
spatial transformations to establish anatomical correspondence between pairs or
groups of images. Recently, deep learning-based image registration methods have
been widely explored, and demonstrated to enable fast and accurate image
registration in a variety of applications. However, most deep learning-based
registration methods assume that the deformation fields are smooth and
continuous everywhere in the image domain, which is not always true, especially
when registering images whose fields of view contain discontinuities at
tissue/organ boundaries. In such scenarios, enforcing smooth, globally
continuous deformation fields leads to incorrect/implausible registration
results. We propose a novel discontinuity-preserving image registration method
to tackle this challenge, which ensures globally discontinuous and locally
smooth deformation fields, leading to more accurate and realistic registration
results. The proposed method leverages the complementary nature of image
segmentation and registration and enables joint segmentation and pair-wise
registration of images. A co-attention block is proposed in the segmentation
component of the network to learn the structural correlations in the input
images, while a discontinuity-preserving registration strategy is employed in
the registration component of the network to ensure plausibility in the
estimated deformation fields at tissue/organ interfaces. We evaluate our method
on the task of intra-subject spatio-temporal image registration using
large-scale cinematic cardiac magnetic resonance image sequences, and
demonstrate that our method achieves significant improvements over the
state-of-the-art for medical image registration, and produces high-quality
segmentation masks for the regions of interest.
