Evaluation of Unsupervised Deformable Image Registration Using CNN and ViT on 4D-CT.
Authors
Affiliations (1)
Affiliations (1)
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.
Abstract
Deformable image registration is essential in medical image analysis. The state-of-the-art approaches are unsupervised methods based on convolutional neural networks (CNN) and vision transformers (ViT). While CNNs perform well in extracting local features, ViTs perform better in extracting global features. This study aimed to compare the performance of CNN and ViT in unsupervised deformable image registration. We have proposed a unified registration framework and evaluated both architectures. Experiments have been conducted using 4D-CT. The results have shown ViT-based registration to achieve superior performance compared to CNN-based methods. The findings have indicated vision transformer architectures to be more effective than convolutional networks for unsupervised deformable registration on 4D-CT data. Foundation Item: This work is supported by the National Natural Science Foundation of China (No.61801413).