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.
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.
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
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.