Automated dendritic spine detection using convolutional neural networks on maximum intensity projected microscopic volumes.

ABSTRACT:

Background

Dendritic spines are structural correlates of excitatory synapses in the brain. Their density and structure are shaped by experience, pointing to their role in memory encoding. Dendritic spine imaging, followed by manual analysis, is a primary way to study spines. However, an approach that analyses dendritic spines images in an automated and unbiased manner is needed to fully capture how spines change with normal experience, as well as in disease.

New method

We propose an approach based on fully convolutional neural networks (FCNs) to detect dendritic spines in two-dimensional maximum-intensity projected images from confocal fluorescent micrographs. We experiment on both fractionally strided convolution and efficient sub-pixel convolutions. Dendritic spines far fr

SUBMITTER: Xiao X 

PROVIDER: S-EPMC6402488 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature