  Despite current advancement in the field of biomedical image processing,
propelled by the deep learning revolution, multimodal image registration, due
to its several challenges, is still often performed manually by specialists.
The recent success of image-to-image (I2I) translation in computer vision
applications and its growing use in biomedical areas provide a tempting
possibility of transforming the multimodal registration problem into a,
potentially easier, monomodal one. We conduct an empirical study of the
applicability of modern I2I translation methods for the task of multimodal
biomedical image registration. We compare the performance of four Generative
Adversarial Network (GAN)-based methods and one contrastive representation
learning method, subsequently combined with two representative monomodal
registration methods, to judge the effectiveness of modality translation for
multimodal image registration. We evaluate these method combinations on three
publicly available multimodal datasets of increasing difficulty, and compare
with the performance of registration by Mutual Information maximisation and one
modern data-specific multimodal registration method. Our results suggest that,
although I2I translation may be helpful when the modalities to register are
clearly correlated, registration of modalities which express distinctly
different properties of the sample are not well handled by the I2I translation
approach. When less information is shared between the modalities, the I2I
translation methods struggle to provide good predictions, which impairs the
registration performance. The evaluated representation learning method, which
aims to find an in-between representation, manages better, and so does the
Mutual Information maximisation approach. We share our complete experimental
setup as open-source (https://github.com/Noodles-321/Registration).
