SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration
This work addresses challenges in medical image analysis for deformable registration, offering improved accuracy and efficiency, but it is incremental as it builds on existing unsupervised deep learning methods.
The paper tackled the problem of uncorrelated features, poor registration of large deformations and details, and unnatural deformation fields in unsupervised deep learning for deformable image registration by proposing SearchMorph, a multi-scale correlation iterative network, which achieved the highest registration accuracy and lowest folding point ratio with short elapsed time compared to state-of-the-art methods.
Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis. The unsupervised deep learning registration method can quickly achieve high registration accuracy without labels. However, these methods generally suffer from uncorrelated features, poor ability to register large deformations and details, and unnatural deformation fields. To address the issues above, we propose an unsupervised multi-scale correlation iterative registration network (SearchMorph). In the proposed network, we introduce a correlation layer to strengthen the relevance between features and construct a correlation pyramid to provide multi-scale relevance information for the network. We also design a deformation field iterator, which improves the ability of the model to register details and large deformations through the search module and GRU while ensuring that the deformation field is realistic. We use single-temporal brain MR images and multi-temporal echocardiographic sequences to evaluate the model's ability to register large deformations and details. The experimental results demonstrate that the method in this paper achieves the highest registration accuracy and the lowest folding point ratio using a short elapsed time to state-of-the-art.