However, human intervention is still required in all cases at least to ensure error checking and quality control 17. Available tools such as Imaris (Bitplane), FIJI Neurphology 14, and FIJI NeuronJ 15, and an implementation of a hidden Markov model 16 depend on image quality, culture complexity, and signal strength. To alleviate this manual annotation step and to improve the robustness and objectivity of the measurements, many groups have focused on automating some or all of the steps necessary for downstream neurite quantification 11– 13. This explains why neurites are not routinely assayed in studies and when they are, very limited sampling is performed. These factors make hand tracing of neurites, the gold standard in neurite annotation, an extremely challenging task 10. However, accurately measuring neurite length and branching is challenging, as neurites are thin and irregular, and their dimness creates an inherent signal to noise constraint. In cell culture, neuronal soma size, neurite length, and branching complexity can all be measured 9 to evaluate physiological responses to perturbagens and potential treatments. Indeed, neuronal morphology can be a stronger marker than protein aggregates and plaques in determining early disease onset 3– 7, and understanding the pace and extension of neurite loss could lead to novel therapeutic interventions for neurodegenerative disease 8. The study of dendritic length and complexity, and spine density has become standard in the analysis of neuronal abnormalities, since alteration of these structures underlies many neurological diseases 2. The number, dynamics and longevity of neuronal connections are informative to neuronal communication, synaptic plasticity, and normal brain function 1. Neuronal morphology plays an important role in nervous system function, from how the brain forms connections in development to how the brain changes in response to disease. This approach makes accurate analysis of large or longitudinal datasets feasible across a broad range of datasets. With this algorithm we developed an approach to quantify neurites with an accuracy that nears and sometimes exceeds human curation, in 1/100 th of the time. We also defined a sequence of steps to generate custom models with a small number of annotation inputs, and extended the predictions to a 3D tissue sample and longitudinal imaging. Based on a dataset with partial annotation, NAPA generated predictions on several unannotated datasets, and was able to capture differences between disease and control conditions. ![]() NAPA overcomes experimental variation inherent to fluorescence imaging by learning more broader features that are important for neurite recognition. In order to facilitate neurite quantification, we developed a deep learning (DL) neurite annotation prediction algorithm (NAPA) to predict the structure and length of neurites. Moreover, the tools available to aid this aim are limited in their capacity to generalize to high throughput image acquisition such as time-lapse or longitudinal imaging, where imaging conditions can change dramatically over the course of the experiment. The neurites extending from cell soma can be quite thin, dim, overlapping and complex, making them laborious to trace manually and difficult to annotate and quantify computationally or automatically. These changes are typically detectable by microscopy in cell culture or histological samples, but quantification can be challenging. Changes to neuronal morphology and loss of neurites and synaptic connections can be an important early indicator of neurological diseases, and a pathognomonic sign of neurodevelopmental disorders.
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