Deep Neurite Analysis Tool (DeNAT): A machine-learning framework for precise automated neurite outgrowth measurement

Abstract

Accurate quantification of neurite sprouting after injury is a critical step in axon regeneration research. Yet it remains a major bottleneck, as the current gold standard relies on manual counting by multiple blinded observers. This process is slow, labor-intensive, and prone to variability. While some software can measure total neurite length, they arent made to specifically measure new growth in complicated images from real-life injury models, like the thoracic crush and pyramidotomy model. Existing software can measure total neurite length in culture, but it is not designed to capture new growth in complex images from injury models, such as thoracic crush or pyramidotomy. Crucially, these tools lack the ability to selectively analyze growth within user-defined regions, a key requirement for injury paradigms. To address this need, we developed the Deep Neurite Analysis Tool (DeNAT), an accessible deep-learning-based platform that automatically measures neurite outgrowth after injury. DeNAT allows users to define regions of interest to specifically quantify sprouting in images from common spinal cord injury paradigms. We benchmarked its performance against manual scoring and conventional automated approaches. DeNAT achieved 87 percent accuracy in detecting neurite sprouts relative to manual counts, while reducing variability and labor. By combining user-guided region selection with automated deep learning analysis, DeNAT offers an accurate, reproducible, and efficient solution for measuring neurite outgrowth in injury models.