Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks.

ABSTRACT: Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available, (b) subject to error with significant brain atrophy, cerebrovascular disease and lesions, and/or (c) computationally expensive or require parameter tuning. In this study, we trained a 3D convolutional neural network using 259 bilateral manually delineated segmentations collected from three studies, acquired at multiple sites on different scanners with variable protocols. Our training dataset consisted of elderly cases difficult to segment due to extensive atrophy, vascular disease, and lesions. Our algorithm, (HippMapp3r), was validated agains

SUBMITTER: Goubran M 

PROVIDER: S-EPMC7267905 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature