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

• Published in: arXiv.org
• Main Authors: Dey, Neel, Schlemper, Jo, Seyed Sadegh Mohseni Salehi, Zhou, Bo, Gerig, Guido, Sofka, Michal
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.06.2022
• Subjects: Ablation; Computer vision; Deformation; Domains; Formability; Image registration; Learning; Regularization; Representations

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 results with smooth and invertible deformations across a series of baselines and ablations on a neonatal T1-T2 brain MRI registration task with all methods validated over a wide range of deformation regularization strengths.