Recursive Cascaded Networks for Unsupervised Medical Image Registration

Abstract

We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration. The proposed architecture is simple in design and can be built on any base network. The moving image is warped successively by each cascade and finally aligned to the fixed image; this procedure is recursive in a way that every cascade learns to perform a progressive deformation for the current warped image. The entire system is end-to-end and jointly trained in an unsupervised manner. In addition, enabled by the recursive architecture, one cascade can be iteratively applied for multiple times during testing, which approaches a better fit between each of the image pairs. We evaluate our method on 3D medical images, where deformable registration is most commonly applied. We demonstrate that recursive cascaded networks achieve consistent, significant gains and outperform state-of-the-art methods. The performance reveals an increasing trend as long as more cascades are trained, while the limit is not observed. Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.

arXiv commentAccepted to ICCV 2019

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• TensorFlow: A system for large-scale machine learning
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
• VoxelMorph: A Learning Framework for Deformable Medical Image Registration
• The Medical Segmentation Decathlon
• A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
• An Unsupervised Learning Model for Deformable Medical Image Registration
• End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
• Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
• Non-rigid image registration using fully convolutional networks with deep self-supervision
• Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
• Deep Deformation Network for Object Landmark Localization

Cited by in corpus (30)

• Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network
• Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
• Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
• Learning the Effect of Registration Hyperparameters with HyperMorph
• CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond
• Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning
• One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
• Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer
• Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy
• Recursive Deformable Image Registration Network with Mutual Attention
• Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision
• Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
• Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy
• An Auto-Context Deformable Registration Network for Infant Brain MRI
• Deep learning based CT-to-CBCT deformable image registration for autosegmentation in head and neck adaptive radiation therapy
• Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
• Deep Implicit Optimization enables Robust Learnable Features for Deformable Image Registration
• Test-Time Training for Deformable Multi-Scale Image Registration
• Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
• ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
• ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
• One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
• KLDivNet: An unsupervised neural network for multi-modality image registration
• DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images
• Cascaded Feature Warping Network for Unsupervised Medical Image Registration
• Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
• MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network
• SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
• Multi-scale Neural ODEs for 3D Medical Image Registration