English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Establishing voxelwise semantic correspondence across distinct imagingmodalities is a foundational yet formidable computer vision task. Currentmulti-modality registration techniques maximize hand-crafted inter-domainsimilarity functions, are limited in modeling nonlinear intensity-relationshipsand deformations, and may require significant re-engineering or underperform onnew tasks, datasets, and domain pairs. This work presents ContraReg, anunsupervised contrastive representation learning approach to multi-modalitydeformable registration. By projecting learned multi-scale local patch featuresonto a jointly learned inter-domain embedding space, ContraReg obtainsrepresentations useful for non-rigid multi-modality alignment. Experimentally,ContraReg achieves accurate and robust results with smooth and invertibledeformations across a series of baselines and ablations on a neonatal T1-T2brain MRI registration task with all methods validated over a wide range ofdeformation regularization strengths.

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