Anatomical Correspondence

Anatomical correspondence aims to precisely map corresponding anatomical locations across different medical images, a crucial step for various image analysis tasks. Current research focuses on developing robust algorithms, often leveraging deep learning architectures like self-supervised embedding models and contrastive learning, to overcome challenges posed by modality variations and complex anatomical structures. These advancements improve the accuracy and efficiency of image registration, enabling more precise diagnoses, treatment planning, and longitudinal studies across diverse imaging modalities. The resulting improvements in anatomical alignment have significant implications for medical image analysis and image-guided interventions.