Related Experiment Video
Updated: Jan 14, 2026
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Evaluation of Unsupervised Deformable Image Registration using CNN and ViT on 4D-CT
Peizhi Chen1, Jialan Wang1, Yifan Guo1
1College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.
Introduction:
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.
Objective:
This study aimed to compare the performance of CNN and ViT in unsupervised deformable image registration.
Method:
We have proposed a unified registration framework and evaluated both architectures. Experiments have been conducted using 4D-CT.
Results:
The results have shown ViT-based registration to achieve superior performance compared to CNN-based methods.
Conclusion:
The findings have indicated vision transformer architectures to be more effective than convolutional networks for unsupervised deformable registration on 4D-CT data.
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Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
01:10Computed Tomography
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...