Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration

In this study, we propose a mean-teacher based registration framework, which incorporates an additional temporal consistency regularization term by encouraging the teacher model's prediction to be consistent with that of the student model. More importantly, instead of searching for a fixed weight, the teacher enables automatically adjusting the weights of the spatial regularization and the temporal consistency regularization by taking advantage of the transformation uncertainty and appearance uncertainty. Extensive experiments on the challenging abdominal CT-MRI registration show that our training strategy can promisingly advance the original learning-based method in terms of efficient hyperparameter tuning and a better tradeoff between accuracy and smoothness.PMID:37250854 | PMC:PMC10211410

Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 30, 2023 Category: Radiology Authors: Zhe Xu Jie Luo Donghuan Lu Jiangpeng Yan Sarah Frisken Jayender Jagadeesan William M Wells Xiu Li Yefeng Zheng Raymond Kai-Yu Tong Source Type: research