BranchAnalysis2D/3D automates morphometry analyses of branching structures - Srinivasan, Aditya; Muñoz-Estrada, Jesús; Bourgeois, Justin R.; Nalwalk, Julia W.; Pumiglia, Kevin M.; Sheen, Volney L.; Ferland, Russell J. (Journal of neuroscience methods, 2018)
•An open-source algorithm for analysis of branching structures is presented.•Algorithm output matches output from human observers using existing analysis tools.•The algorithm is faster than human observers using other analysis tools.•BranchAnalysis2D/3D automation decreases investigator bias.•BranchAnalysis2D/3D can be used to measure any branching structure. Morphometric analyses of biological features have become increasingly common in recent years with such analyses being subject to a large degree of observer bias, variability, and time consumption. While commercial software packages exist to perform these analyses, they are expensive, require extensive user training, and are usually dependent on the observer tracing the morphology. To address these issues, we have developed a broadly applicable, no-cost ImageJ plugin we call ‘BranchAnalysis2D/3D’, to perform morphometric analyses of structures with branching morphologies, such as neuronal dendritic spines, vascular morphology, and primary cilia. Our BranchAnalysis2D/3D algorithm allows for rapid quantification of the length and thickness of branching morphologies, independent of user tracing, in both 2D and 3D data sets. We validated the performance of BranchAnalysis2D/3D against pre-existing software packages using trained human observers and images from brain and retina. We found that the BranchAnalysis2D/3D algorithm outputs results similar to available software (i.e., Metamorph, AngioTool, Neurolucida), while allowing faster analysis times and unbiased quantification. BranchAnalysis2D/3D allows inexperienced observers to output results like a trained observer but more efficiently, thereby increasing the consistency, speed, and reliability of morphometric analyses.