NeuroRetriever: Automatic Neuron Segmentation for Connectome Assembly

Abstract

Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias. In this study, we report an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of confocal fluorescence images of single neurons in the adult Drosophila brain. NeuroRetriever uses a high-dynamic-range thresholding method to segment three-dimensional morphology of single neurons based on branch-specific structural features. Applying NeuroRetriever to automatically segment single neurons in 22,037 raw brain images, we successfully retrieved 28,125 individual neurons validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

“…Traditionally in light microscopy (confocal, light sheet, and STED), neuron instance individualization starts with filament tracing methods ( Xiao and Peng, 2013 ; Feng et al, 2015 ; Liu et al, 2016 ; Quan et al, 2016 ; Shih et al, 2021 ). These methods work as an initial segmentation and are responsible for differentiating the neural mass foreground from the noisy scattered background ( Magliaro et al, 2019 ).…”