RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations - Wu, Jingpeng; Turner, Nicholas; Bae, J. Alexander; Vishwanathan, Ashwin; Seung, H. Sebastian (Frontiers in Neuroinformatics, 2022)
Benefitting from the rapid development of Electron Microscopy imaging and Deep Learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. In automated segmentation, neurons are represented as clusters of image pixels for each neuron rather than skeletons from manual tracing. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale Electron Microscopy image segmentation datasets.