{Reference Type}: Journal Article
{Title}: Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review.
{Author}: Leite J;Nhoatto F;Jacob A;Santana R;Lobato F;
{Journal}: Neuroinformatics
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jun 26
{Factor}: 2.864
{DOI}: 10.1007/s12021-024-09674-6
{Abstract}: Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain.