Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration

TL;DR

This paper introduces an unsupervised deep learning framework for deformable image registration that enforces inverse consistency and anti-folding constraints, improving registration accuracy without requiring ground-truth data.

Contribution

It proposes an inverse-consistent deep network with novel constraints to ensure diffeomorphic transformations, addressing limitations of previous unsupervised registration methods.

Findings

Outperforms state-of-the-art methods in MRI registration tasks

Effectively enforces inverse consistency and anti-folding constraints

Achieves superior accuracy in tissue segmentation and landmark detection

Abstract

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. Also, existing approaches usually encourage the to-be-estimated transformation to be locally smooth via a smoothness constraint only, which could not completely avoid folding in the resulting transformation. To this end, we propose an Inverse-Consistent deep Network (ICNet) for unsupervised deformable image registration. Specifically, we develop an inverse-consistent constraint to encourage that a pair of images are symmetrically deformed toward one another, until both warped images are matched. Besides using the conventional smoothness constraint, we also propose an anti-folding constraint to further avoid folding in the transformation. The proposed method does not require any supervision information, while encouraging the diffeomoprhic property of the transformation via the proposed inverse-consistent and anti-folding constraints. We evaluate our method on T1-weighted brain magnetic resonance imaging (MRI) scans for tissue segmentation and anatomical landmark detection, with results demonstrating the superior performance of our ICNet over several state-of-the-art approaches for deformable image registration. Our code will be made publicly available.