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Abstract #3467

Contrastive Learning of Inter-domain Similarity for Unsupervised Multi-modality Deformable Registration

Neel Dey1, Jo Schlemper2, Seyed Sadegh Mohseni Salehi2, Bo Zhou3, and Michal Sofka2
1Computer Science and Engineering, New York University, New York City, NY, United States, 2Hyperfine, New York City, NY, United States, 3Yale University, New Haven, CT, United States


We propose an unsupervised contrastive representation learning framework for deformable and diffeomorphic multi-modality MR image registration. The proposed deep network and data-driven objective function yield improved registration performance in terms of anatomical volume overlap over several previous hand-crafted objectives such as Mutual Information and others. For fair comparison, our experiments train all methods over the entire range of a key registration hyperparameter controlling deformation smoothness using conditional registration hypernetworks. T1w and T2w brain MRI registration improvements are presented across a large cohort of 1041 high-field 3T research-grade acquisitions while maintaining comparable deformation smoothness and invertibility characteristics to previous methods.

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