%0 Journal Article
%T Unsupervised Imbalanced Registration for Enhancing Accuracy and Stability in Medical Image Registration.
%A Chen P
%A Lin J
%A Guo Y
%A Pei X
%J Curr Med Imaging
%V 0
%N 0
%D 2024 Jan 11
%M 38258590
暂无%R 10.2174/0115734056265001231122110350
%X <B>BACKGROUND: </B>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.<BR><B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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%.<BR><B>CONCLUSIONS: </B>Unsupervised Imbalanced Registration is a promising approach that is compatible with current unsupervised image registration methods applied to 4D images.