Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration

Understand

Deformable image registration is a fundamental task in medical image analysis, aiming to establish a dense and non-linear correspondence between a pair of images.

• Previous deep-learning studies usually employ supervised neural networks to directly learn the spatial transformation from one image to another, requiring task-specific ground-truth registration for model training.
• Due to the difficulty in collecting precise ground-truth registration, implementation of these supervised methods is practically challenging.
• Although several unsupervised networks have been recently developed, these methods usually ignore the inherent inverse-consistent property (essential for diffeomorphic mapping) of transformations between a pair of images.