Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration

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.

arXiv comment13 pages, 11 figures

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• Unsupervised End-to-end Learning for Deformable Medical Image Registration

Cited by in corpus (21)

• TransMorph: Transformer for unsupervised medical image registration
• Deep Learning in Medical Image Registration: A Review
• Medical image registration using unsupervised deep neural network: A scoping literature review
• Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth
• CartiMorph: a framework for automated knee articular cartilage morphometrics
• FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration
• CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
• Unsupervised Deformable Image Registration Using Cycle-Consistent CNN
• Contrastive Registration for Unsupervised Medical Image Segmentation
• MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent
• FIRE: Unsupervised bi-directional inter-modality registration using deep networks
• SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
• Deformable Image Registration using Unsupervised Deep Learning for CBCT-guided Abdominal Radiotherapy
• On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration Networks
• Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
• Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms
• ProsRegNet: A Deep Learning Framework for Registration of MRI and Histopathology Images of the Prostate
• Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images
• Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches
• MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network
• Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods