UNSUPERVISED END-TO-END GROUPWISE REGISTRATION FRAMEWORK WITHOUT GENERATING TEMPLATES

Groupwise registration is an important and challenging task for medical image processing and analysis. Traditional methods focus on generating a template and performing pairwise registration, which can be time-consuming to converge. In this paper, we propose an unsupervised end-to-end groupwise registration framework with multi-step mechanisms to progressively refine outputs. The framework can generate the displacement field for each subject directly without templates. Customized loss functions are designed to optimize the model and reduce the bias of generated common space. We experiment on 2D brain MRI coronal slices from OASIS and compare the results with two baseline methods using Dice score criterion. Results show that our framework achieves state-of-the-art performance with a much lower time cost.