  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 rigid
registration of multimodal biomedical and medical 2D and 3D images. We compare
the performance of four Generative Adversarial Network (GAN)-based I2I
translation 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 four publicly available
multimodal (2D and 3D) datasets and compare them with the performance of
registration achieved by several well-known approaches acting directly on
multimodal image data. 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 is
not well handled by the I2I translation approach. The evaluated representation
learning method, which aims to find abstract image-like representations of the
information shared between the modalities, manages better, and so does the
Mutual Information maximisation approach, acting directly on the original
multimodal images. We share our complete experimental setup as open-source
(https://github.com/MIDA-group/MultiRegEval).
