Recursive Decomposition Network for Deformable Image Registration

Deformation decomposition serves as a good solution for deformable image registration when the deformation is large. Current deformation decomposition methods can be categorized into cascade-based methods and pyramid-based methods. However, cascade-based methods suffer from heavy computational burdens and long inference time due to their structures of repeated subnetworks, while the effectiveness of pyramid-based methods is constrained by their limited numbers of resolution levels. In this paper, to address both the insufficient and inefficient decomposition problems in current deformation decomposition methods, we propose a recursive decomposition network (RDN) to offer a novel solution for deformable image registration. Stage-wise recursion can efficiently decompose a large deformation into different pyramid estimation stages without using repeated subnetworks like in cascade-based methods. Level-wise recursion can sufficiently decompose the deformation inside each resolution level instead of only one-time estimation like in pyramid-based methods. Extensive experiments and ablation studies on two representative datasets validate the effectiveness and efficiency of our proposed RDN.

Authors

• Bo Hu East China Normal University
• Shenglong Zhou University of Science and Technology of China
• Zhiwei Xiong MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China Institute of Artificial Intelligence, Hefei Comprehensive National Science Center
• Feng Wu

Keywords

• Strain
• Image registration
• Optimization
• Bioinformatics
• Image resolution
• Measurement
• Mathematical models
• Deformable Image Registration
• Large Deformation
• Level Of Resolution
• Inference Time
• Heavy Computational Burden
• Deformable Registration
• Convolutional Neural Network
• Similarity Measure
• Receptive Field
• Image Pairs
• Unsupervised Methods
• Convolutional Neural Network Model
• Baseline Methods
• Space Complexity
• Voxel Volume
• Small Deformation
• CNN-based Methods
• Registration Method
• Medical Image Analysis
• Deformation Field
• Dice Score
• Multi-level Features
• Deformation Model
• GPU Memory
• Computational Consumption
• Decomposition Step
• Output Level
• Feature Learning
• Trilinear Interpolation
• deformation decomposition
• Algorithms
• Humans
• Image Processing, Computer-Assisted