Random-Reaction-Seed Method for Automated Identification of Neurite Elongation and Branching

Alvason Zhenhua Li,Lawrence Corey,Jia Zhu

doi:10.1038/s41598-019-39962-0

Alvason Zhenhua Li, Lawrence Corey + Show 1 more

Open Access

https://doi.org/10.1038/s41598-019-39962-0

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Abstract

Conventional deterministic algorithms (i.e., skeletonization and edge-detection) lack robustness and sensitivity to reliably detect the neurite elongation and branching of low signal-to-noise-ratio microscopy images. Neurite outgrowth experiments produce an enormous number of images that require automated measurement; however, the tracking of neurites is easily lost in the automated process due to the intrinsic variability of neurites (either axon or dendrite) under stimuli. We have developed a stochastic random-reaction-seed (RRS) method to identify neurite elongation and branching accurately and automatically. The random-seeding algorithm of RRS is based on the hidden-Markov-model (HMM) to offer a robust enough way for tracing arbitrary neurite structures, while the reaction-seeding algorithm of RRS secures the efficiency of random seeding. It is noteworthy that RRS is capable of tracing a whole neurite branch by only one initial seed, so that RRS is proficient at quantifying extensive amounts of neurite outgrowth images with noisy background in microfluidic devices of biomedical engineering fields. The method also showed notable performance for reconstructing of net-like structures, and thus is expected to be proficient for biomedical feature extractions in a wide range of applications, such as retinal vessel segmentation and cell membrane profiling in spurious-edge-tissues.

Highlights

Neurons undergo the most complicated morphogenesis of all cells in a developing organism
Www.nature.com/scientificreports efficient Random-Reaction-Seed method (RRS), which underlies the effectiveness of random seeding that occurs in employing the maximum likelihood method in statistical estimation for probabilistic functions of Markov chain
The RRS method has demonstrated the excellent performance in planar image analysis; the RRS method could be extended to 3D image analysis

Summary

Introduction

Neurons undergo the most complicated morphogenesis of all cells in a developing organism. A time-consuming preparation is unavoidable, because a proper skeleton/edge extracted from an image requires an interplay of sharpness adjustments (e.g., threshold level). This process becomes unproductive for a long-stitched image combined with different exposure sub-images. Despite ongoing algorithm improvements, automated efficient methods for neurite identification in low SNR images is typically challenging due to the intrinsic variability of neurite (either axon or dendrite) under stimuli. Efficient Random-Reaction-Seed method (RRS), which underlies the effectiveness of random seeding that occurs in employing the maximum likelihood method in statistical estimation for probabilistic functions of Markov chain

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