Unsupervised Imbalanced Registration for Enhancing Accuracy and Stability in Medical Image Registration.
Curr Med Imaging
; 2024 Jan 11.
Article
em En
| MEDLINE
| ID: mdl-38258590
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Curr Med Imaging
Ano de publicação:
2024
Tipo de documento:
Article