ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration.

ABSTRACT: Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task. Current multi-modality registration techniques maximize hand-crafted inter-domain similarity functions, are limited in modeling nonlinear intensity-relationships and deformations, and may require significant re-engineering or underperform on new tasks, datasets, and domain pairs. This work presents ContraReg, an unsupervised contrastive representation learning approach to multi-modality deformable registration. By projecting learned multi-scale local patch features onto a jointly learned inter-domain embedding space, ContraReg obtains representations useful for non-rigid multi-modality alignment. Experimentally, ContraReg achieves accurate and robust resu

SUBMITTER: Dey N 

PROVIDER: S-EPMC10415941 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature