A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning

摘要

Background: Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.