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The Use of Neural Networks and Texture Analysis for Rapid Objective Selection of Regions of Interest in Cytoskeletal Images

Published online by Cambridge University Press:  12 January 2012

Amanda D. Felder Derkacs ,
Samuel R. Ward  and
Richard L. Lieber
Amanda D. Felder Derkacs
Affiliation:
Department of Bioengineering, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0863, USA
Samuel R. Ward
Affiliation:
Departments of Radiology, Orthopaedic Surgery, and Bioengineering, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0610, USA
Richard L. Lieber*
Affiliation:
Departments of Orthopaedic Surgery and Bioengineering, V.A. Medical Center, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0863, USA
*
Corresponding author. E-mail: rlieber@ucsd.edu
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Abstract

Understanding cytoskeletal dynamics in living tissue is prerequisite to understanding mechanisms of injury, mechanotransduction, and mechanical signaling. Real-time visualization is now possible using transfection with plasmids that encode fluorescent cytoskeletal proteins. Using this approach with the muscle-specific intermediate filament protein desmin, we found that a green fluorescent protein–desmin chimeric protein was unevenly distributed throughout the muscle fiber, resulting in some image areas that were saturated as well as others that lacked any signal. Our goal was to analyze the muscle fiber cytoskeletal network quantitatively in an unbiased fashion. To objectively select areas of the muscle fiber that are suitable for analysis, we devised a method that provides objective classification of regions of images of striated cytoskeletal structures into “usable” and “unusable” categories. This method consists of a combination of spatial analysis of the image using Fourier methods along with a boosted neural network that “decides” on the quality of the image based on previous training. We trained the neural network using the expert opinion of three scientists familiar with these types of images. We found that this method was over 300 times faster than manual classification and that it permitted objective and accurate classification of image regions.

Keywords

image analysisclassificationskeletal muscleconfocal microscopy
Type
Feature Article
Information
Microscopy and Microanalysis , Volume 18 , Issue 1 , February 2012 , pp. 115 - 122
Copyright
Copyright © Microscopy Society of America 2012

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