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

Journal: Journal of neuroscience methods
PMID: 30130608

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.

Authors

  • Xuerong Xiao
    Department of Electrical Engineering, Stanford University, David Packard Building, 350 Serra Mall, Stanford, CA 94305, USA. Electronic address: xuerong@stanford.edu.
  • Maja Djurisic
    Departments of Biology and Neurobiology, and Bio-X, Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305, USA.
  • Assaf Hoogi
  • Richard W Sapp
    Departments of Biology and Neurobiology, and Bio-X, Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305, USA.
  • Carla J Shatz
    Departments of Biology and Neurobiology, and Bio-X, Stanford University, James H. Clark Center, 318 Campus Dr, Stanford, CA 94305, USA.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.

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