Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation.

ABSTRACT: White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D convolutional neural network with a U-Net architecture

SUBMITTER: Mojiri Forooshani P 

PROVIDER: S-EPMC8996363 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature