An Adaptive Region-Based Transformer for Nonrigid Medical Image Registration With a Self-Constructing Latent Graph

Nonrigid registration of medical images is formulated usually as an optimization problem with the aim of seeking out the deformation field between a referential–moving image pair. During the past several years, advances have been achieved in the convolutional neural network (CNN)-based registration of images, whose performance was superior to most conventional methods. More lately, the long-range spatial correlations in images have been learned by incorporating an attention-based model into the transformer network. However, medical images often contain plural regions with structures that vary in size. The majority of the CNN-and transformer-based approaches adopt embedding of patches that are identical in size, disallowing representation of the inter-regional structural disparities within an image. Besides, it probably leads to the structural and semantical inconsistencies of objects as well. To address this issue, we put forward an innovative module called region-based structural relevance embedding (RSRE), which allows adaptive embedding of an image into unequally-sized structural regions based on the similarity of self-constructing latent graph instead of utilizing patches that are identical in size. Additionally, a transformer is integrated with the proposed module to serve as an adaptive region-based transformer (ART) for registering medical images nonrigidly. As demonstrated by the experimental outcomes, our ART is superior to the advanced nonrigid registration approaches in performance, whose Dice score is 0.734 on the LPBA40 dataset with $0.318\%$ foldings for deformation field, and is 0.873 on the ADNI dataset with $0.331\%$ foldings.