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Abstract #3741

Assessment of Uncertainty in Brain MRI Deformable Registration

Samah Khawaled1 and Moti Freiman2
1Applied Mathematics, Technion, Haifa, Israel, 2Biomedical Engineering, Technion, Haifa, Israel

Unsupervised deep neural networks (DNN) are successfully employed to predict deformation-fields in neuroimaging studies. Bayesian DNN models enable safer utilization of DNN methods in neuroimaging studies, improve generalization and enable assessment of uncertainty in the predictions. We propose a non-parametric Bayesian approach to estimate the uncertainty in DNN-based algorithms for brain MRI deformable registration. We demonstrated the added-value of our Bayesian registration framework on the brain MRI (LPBA40) dataset compared to state-of-the-art VoxelMorph DNN. Further, we quantified the uncertainty of the registration and assessed its correlation with the out-of-distribution data.

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