Multi-modal image registration spatially aligns two images with different
distributions. One of its major challenges is that images acquired from
different imaging machines have different imaging distributions, making it
difficult to focus only on the spatial aspect of the images and ignore
differences in distributions. In this study, we developed a self-supervised
approach, Indescribable Multi-model Spatial Evaluator (IMSE), to address
multi-modal image registration. IMSE creates an accurate multi-modal spatial
evaluator to measure spatial differences between two images, and then optimizes
registration by minimizing the error predicted of the evaluator. To optimize
IMSE performance, we also proposed a new style enhancement method called
Shuffle Remap which randomizes the image distribution into multiple segments,
and then randomly disorders and remaps these segments, so that the distribution
of the original image is changed. Shuffle Remap can help IMSE to predict the
difference in spatial location from unseen target distributions. Our results
show that IMSE outperformed the existing methods for registration using T1-T2
and CT-MRI datasets. IMSE also can be easily integrated into the traditional
registration process, and can provide a convenient way to evaluate and
visualize registration results. IMSE also has the potential to be used as a new
paradigm for image-to-image translation. Our code is available at
this https URL

本文研究了多模态图像配准的挑战，介绍了一种基于自监督学习的 IMSE 方法，利用空间评估器和 Shuffle Remap 方法来解决这个问题，证明了它的优越性能，并提出了将它作为图像转换新范式的潜力。