ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration
This addresses the challenge of multi-modality image registration in medical imaging, which is incremental as it builds on contrastive learning methods for this specific domain.
The paper tackles the problem of establishing voxelwise semantic correspondence across distinct imaging modalities for deformable image registration, presenting ContraReg, an unsupervised contrastive learning approach that achieves accurate and robust results with smooth and invertible deformations on a neonatal T1-T2 brain MRI registration task.
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.