Résumé
The extraction of metrics quantifying the morphology of the neuronal tree is essentialfor the study of neuronal development and function. However, the extraction of suchmorphometrics often requires manually tracing dendrites from high-resolution microscopyimages, a burdensome and time-consuming task restraining large-scale quantitative studies.In this work, we propose a method based on generative AI to automatically generateannotated microscopy images from existing open datasets of traced neurons. We showthat the annotated data can be used to train a deep-learning model to extract the neuronmorphology, mitigating the need for manual tracing. Finally, we show an application ofdeep-learning models to automatically extract morphometrics and analyze large cohorts ofsingle-neuron Drosophila microscopy images.