Title:Unsupervised Imbalanced Registration for Enhancing Accuracy and Stability in
Medical Image Registration
Volume: 20
Author(s): Peizhi Chen*, Jiacheng Lin, Yifan Guo and Xuan Pei
Affiliation:
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
- Key Laboratory of Pattern Recognition and Image Understanding, Fujian, Xiamen 361024, China
Keywords:
Unsupervised imbalanced registration, Unsupervised image mixtures, Data imbalance, 4d-Lung, Medical image registration, Continuous variable.
Abstract:
Background:
Medical image registration plays an important role in several applications. Existing approaches using unsupervised learning encounter issues due to
the data imbalance problem, as their target is usually a continuous variable.
Objective:
In this study, we introduce a novel approach known as Unsupervised Imbalanced Registration, to address the challenge of data imbalance and
prevent overconfidence while increasing the accuracy and stability of 4D image registration.
Methods:
Our approach involves performing unsupervised image mixtures to smooth the input space, followed by unsupervised image registration to learn
the continual target. We evaluated our method on 4D-Lung using two widely used unsupervised methods, namely VoxelMorph and ViT-V-Net.
Results:
Our findings demonstrate that our proposed method significantly enhances the mean accuracy of registration by 3%-10% on a small dataset while
also reducing the accuracy variance by 10%.
Conclusion:
Unsupervised Imbalanced Registration is a promising approach that is compatible with current unsupervised image registration methods applied to
4D images.
