Semi-automated quantification of axonal densities in labeled CNS tissue

MH Grider, Q Chen, HD Shine - Journal of neuroscience methods, 2006 - Elsevier
MH Grider, Q Chen, HD Shine
Journal of neuroscience methods, 2006Elsevier
Current techniques used to quantify axons often rely upon manual quantification or
potentially expensive commercially available programs for automated quantification. We
describe a computerized method for the detection and quantification of axons in the rat CNS
using readily available free software. Feature J, a java-based plug-in to the imaging software
NIH Image J, faithfully detects linear structures such as axons in confocal or bright-field
images using a Hessian-based algorithm. We validated the method by comparing values …
Current techniques used to quantify axons often rely upon manual quantification or potentially expensive commercially available programs for automated quantification. We describe a computerized method for the detection and quantification of axons in the rat CNS using readily available free software. Feature J, a java-based plug-in to the imaging software NIH Image J, faithfully detects linear structures such as axons in confocal or bright-field images using a Hessian-based algorithm. We validated the method by comparing values obtained by manual and automated analyses of axons induced to grow in response to neurotrophin over-expression in the rat spinal cord. We also demonstrated that the program can be used to quantify neurotrophin-induced growth of lesioned serotonergic axons in the rat cortex, where manual measurement would be impractical due to dense axonal growth. The use of this software suite provided faster and less biased quantification of labeled axons in comparison to manual measurements at no cost.
Elsevier