Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks

Citation:

Xu Z, Yan J, Luo J, Wells W, Li X, Jagadeesan J. Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks. Proc IEEE Int Symp Biomed Imaging. 2021;2021. Copy at http://www.tinyurl.com/2dobo4uk

Date Published:

2021 Apr

Abstract:

The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to automatically learn the similarity metric, which has been shown effective in improving registration performance. However, for the regularization term, most existing multimodal registration approaches still use a hand-crafted formula to impose artificial properties on the estimated deformation field. In this work, we propose a unimodal cyclic regularization training pipeline, which learns task-specific prior knowledge from simpler unimodal registration, to constrain the deformation field of multimodal registration. In the experiment of abdominal CT-MR registration, the proposed method yields better results over conventional regularization methods, especially for severely deformed local regions.

Last updated on 06/14/2022