CorrMorph: Unsupervised Deformable Brain MRI Registration Based on Correlation Mining

Deformable image registration, as a fundamental prerequisite for many medical image analysis tasks, has received considerable attention. However, existing methods suffer from two key issues: 1) single-stream methods that stack moving and fixed images as input are prone to interference from spatial misalignment and style discrepancy, while dual-stream methods that use fully parallel encoders face challenges in learning correlations between images. 2) CNN-based methods are difficult to capture the complex spatial correspondences between images, while Transformer-based methods lack the ability to capture local context information. Therefore, we propose an unsupervised deformable brain MRI registration network, CorrMorph, which achieves reasonable and accurate registration by mining correlations. Specifically, we design a match-fusion strategy that allows the independent extraction of shallow features from the moving and fixed images while capturing their correlations in deeper layers. Furthermore, we propose two novel modules. 1) Correlation Matching Module (CMM), which mines correlations between images to achieve effective feature matching, 2) Feature Transmission Module (FTM), which extracts important spatial features to achieve effective feature transmission. Extensive experiments are conducted on three brain MRI datasets, and the results indicate that our method achieves state-of-the-art performance, with an average improvement of 2. 7% on DSC compared to the representative VoxelMorph.

Authors

• Yuan Chang
• Zheng Li
• Ning Yang Institute of Automation, CAS & School of Artificial Intelligence, UCAS

Keywords

• Feature extraction
• Correlation
• Transformers
• Deformation
• Convolutional neural networks
• Magnetic resonance imaging
• Decoding
• Optimization
• Brain
• Bioinformatics
• Brain Magnetic Resonance Imaging
• Deformable Registration
• Local Information
• Effects Of Characteristics
• Spatial Features
• Image Registration
• Feature Matching
• Dice Similarity Coefficient
• Registration Accuracy
• Shallow Features
• Independent Extractions
• Spatial Correspondence
• Transformer-based Methods
• Convolutional Layers
• Convolution Operation
• Learning-based Methods
• Baseline Methods
• Spatial Attention
• Backbone Network
• Skip Connections
• Deformation Field
• Registration Performance
• Encoder Layer
• Brain MRI Images
• Parallel Branches
• Deep Feature Extraction
• Warped Image
• Encoder Module
• Full Method
• Effective Feature Extraction
• Unsupervised deformable registration
• brain MRI registration
• convolutional neural network
• transformer
• Humans
• Unsupervised Machine Learning
• Algorithms
• Image Processing, Computer-Assisted
• Adult