Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Mar 2:16:828169.
doi: 10.3389/fninf.2022.828169. eCollection 2022.

RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations

Jingpeng Wu  1 , Nicholas Turner  1   2 , J Alexander Bae  1   3 , Ashwin Vishwanathan  1 , H Sebastian Seung  1   2
Affiliations

RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations

Jingpeng Wu et al. Front Neuroinform. .

Abstract

Benefiting 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. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. 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.

Keywords: Julia language; clustering; connectomics; morphological analysis; neuron connectivity; neuron morphology; skeletonization.

PubMed Disclaimer

Conflict of interest statement

HS has financial interests in Zetta AI LLC. This study received assistance from Google, Amazon, and Intel. These companies were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Sparse segmentation after proofreading. (A) Some of the neurons are proofread and the fragments are agglomerated as individual neurons. (B) Some of the proofread neurons are visualized.
FIGURE 2
FIGURE 2
Skeletonization computation in a worker.
FIGURE 3
FIGURE 3
(A) Skeletonize of a single neuron. Note that broken parts were reconnected. (B) All the skeletons with a random color. The spheres represent cell bodies with varying diameters.
FIGURE 4
FIGURE 4
Some morphological features of a single neuron. (A) The morphology of a neuron is visualized in Jupyter Notebook. (B) Histogram of tortuosity of neuron segments. (C) Histogram of neighboring node distance. (D) Histogram of path length to the root node. (E) Sholl analysis. (F) Segment path length versus segment order. (G) Branching angle in radians versus tortuosity of segments. (H) Terminal segment path length versus terminal segment neck-head radius ratio.
FIGURE 5
FIGURE 5
NBLAST classification of neurons. The scale bar in the last image is 100 μm.
FIGURE 6
FIGURE 6
Combine morphological NBLAST clustering and synaptic connectivity. (A) The synaptic connectivity matrix was reordered by hierarchical clustering based on connectivity distance. (B) The synaptic connectivity matrix was reordered according to hierarchical clustering based on the NBLAST score. The synapse number is encoded in the point diameter and color. (C) For each neuron pair, the relationship between NBLAST morphological similarity and number of synapses.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Armañanzas R., Ascoli G. A. (2015). Towards the automatic classification of neurons. Trends Neurosci. 38 307–318. 10.1016/j.tins.2015.02.004 - DOI - PMC - PubMed
    1. Ascoli G. A., Krichmar J. L., Nasuto S. J., Senft S. L. (2001). Generation, description and storage of dendritic morphology data. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356 1131–1145. 10.1098/rstb.2001.0905 - DOI - PMC - PubMed
    1. AWSSDK.jl (2021). JuliaCloud. Available online at: https://github.com/JuliaCloud/AWSSDK.jl (accessed February 1, 2022).
    1. Bae J. A., Mu S., Kim J. S., Turner N. L., Tartavull I., Kemnitz N., et al. (2018). Digital museum of retinal ganglion cells with dense anatomy and physiology. Cell 173 1293.e19–1306.e19. 10.1016/j.cell.2018.04.040 - DOI - PMC - PubMed
    1. Beier T., Pape C., Rahaman N., Prange T., Berg S., Bock D. D., et al. (2017). Multicut brings automated neurite segmentation closer to human performance. Nat. Methods 14 101–102. 10.1038/nmeth.4151 - DOI - PubMed

LinkOut - more resources