Aditya Srinivasan1, Arvind Srinivasan2, Russell J Ferland3. 1. Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, USA. Electronic address: sriniva1@amc.edu. 2. Whitney High School, Rocklin, CA 95765, USA. 3. Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, USA; Department of Neurology, Albany Medical College, Albany, NY 12208, USA; Department of Biomedical Sciences, College of Osteopathic Medicine, University of New England, Biddeford, ME 04005, USA. Electronic address: rferland2@une.edu.
Abstract
BACKGROUND: Sholl analysis has been used to analyze neuronal morphometry and dendritic branching and complexity for many years. While the process has become semi-automated in recent years, existing software packages are still dependent on user tracing and hence are subject to observer bias, variability, and increased user times for analyses. Commercial software packages have the same issues as they also rely on user tracing. In addition, these packages are also expensive and require extensive user training. NEW METHOD: To address these issues, we have developed a broadly applicable, no-cost ImageJ plugin, we call AutoSholl, to perform Sholl analysis on pre-processed and 'thresholded' images. This algorithm extends the already existing plugin in Fiji ImageJ for Sholl analysis by allowing for secondary analysis techniques, such as determining number and length of root, intermediate, and terminal dendrites; functions not currently supported in the existing Sholl Analysis plugin in Fiji ImageJ. RESULTS: The algorithm allows for rapid Sholl analysis in both 2-dimensional and 3-dimensional data sets independent of user tracing. COMPARISON WITH EXISTING METHODS: We validated the performance of AutoSholl against pre-existing software packages using trained human observers and images of neurons. We found that our algorithm outputs similar results as available software (i.e., Bonfire), but allows for faster analysis times and unbiased quantification. CONCLUSIONS: As such, AutoSholl allows inexperienced observers to output results like more trained observers efficiently, thereby increasing the consistency, speed, and reliability of Sholl analyses.
BACKGROUND: Sholl analysis has been used to analyze neuronal morphometry and dendritic branching and complexity for many years. While the process has become semi-automated in recent years, existing software packages are still dependent on user tracing and hence are subject to observer bias, variability, and increased user times for analyses. Commercial software packages have the same issues as they also rely on user tracing. In addition, these packages are also expensive and require extensive user training. NEW METHOD: To address these issues, we have developed a broadly applicable, no-cost ImageJ plugin, we call AutoSholl, to perform Sholl analysis on pre-processed and 'thresholded' images. This algorithm extends the already existing plugin in Fiji ImageJ for Sholl analysis by allowing for secondary analysis techniques, such as determining number and length of root, intermediate, and terminal dendrites; functions not currently supported in the existing Sholl Analysis plugin in Fiji ImageJ. RESULTS: The algorithm allows for rapid Sholl analysis in both 2-dimensional and 3-dimensional data sets independent of user tracing. COMPARISON WITH EXISTING METHODS: We validated the performance of AutoSholl against pre-existing software packages using trained human observers and images of neurons. We found that our algorithm outputs similar results as available software (i.e., Bonfire), but allows for faster analysis times and unbiased quantification. CONCLUSIONS: As such, AutoSholl allows inexperienced observers to output results like more trained observers efficiently, thereby increasing the consistency, speed, and reliability of Sholl analyses.
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Authors: Aditya Srinivasan; Jesús Muñoz-Estrada; Justin R Bourgeois; Julia W Nalwalk; Kevin M Pumiglia; Volney L Sheen; Russell J Ferland Journal: J Neurosci Methods Date: 2017-10-20 Impact factor: 2.390
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