Title
Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.
Abstract
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets.
Year
DOI
Venue
2019
10.1016/j.neuroimage.2019.05.017
NeuroImage
Keywords
Field
DocType
Hippocampus,Magnetic resonance imaging,Longitudinal,Convolutional neural networks,Manual tracing,FreeSurfer
Pattern recognition,Convolutional neural network,Segmentation,Cognitive psychology,Psychology,Human brain,Artificial intelligence,Neuroimaging,Hippocampal formation,Hippocampus
Journal
Volume
ISSN
Citations 
197
1053-8119
1
PageRank 
References 
Authors
0.35
0
24