Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

Journal: Journal of neuroscience methods
Published Date: Apr 15, 2017

Abstract

BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility.

Authors

  • Maryana Alegro
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: maryana.alegro@ucsf.edu.
  • Panagiotis Theofilas
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: panos.theofilas@ucsf.edu.
  • Austin Nguy
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: austin.nguy@ucsf.edu.
  • Patricia A Castruita
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: alejandra.castcap@gmail.com.
  • William Seeley
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: bill.seeley@ucsf.edu.
  • Helmut Heinsen
    Medical School of the University of São Paulo, Av. Reboucas 381, São Paulo, SP 05401-000, Brazil. Electronic address: heinsen@mail.uni-wuerzburg.de.
  • Daniela M Ushizima
    Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA; Berkeley Institute for Data Science, University of California Berkeley, Berkeley, CA 94720, USA. Electronic address: dushizima@lbl.gov.
  • Lea T Grinberg
    Memory and Aging Center, University of California San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA. Electronic address: lea.grinberg@ucsf.edu.

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