ArticleScientific reports2015
Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
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- Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders.Frontiers in neuroscience · 2024Review
- Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review.Journal of medical systems · 2023Review
- Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test.Neuroinformatics · 2023Article
- Centella asiatica (L.) Urban. Attenuates Cell Damage in Hydrogen Peroxide-Induced Oxidative Stress in Transgenic Murine Embryonic Stem Cell Line-Derived Neural-Like Cells: A Preliminary Study for Potential Treatment of Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2023Article
- Article
- Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. elegans.PLoS computational biology · 2021Article
- Article
- Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi.PLoS computational biology · 2019Article
- Random-Reaction-Seed Method for Automated Identification of Neurite Elongation and Branching.Scientific reports · 2019Article
- Computing neurite outgrowth and arborization in superior cervical ganglion neurons.Brain research bulletin · 2019Article
- Quantification of surviving neurons after contusion, dislocation, and distraction spinal cord injuries using automated methods.Journal of experimental neuroscience · 2019Article
- DEFiNE: A Method for Enhancement and Quantification of Fluorescently Labeled Axons.Frontiers in neuroanatomy · 2018Article
- Soma Detection in 3D Images of Neurons using Machine Learning Technique.Neuroinformatics · 2018Article
- Tomographic brain imaging with nucleolar detail and automatic cell counting.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image analysis software is an essential tool used in neuroscience and neural engineering to evaluate changes in neuronal structure following extracellular stimuli. Both manual and automated methods in current use are severely inadequate at detecting and quantifying changes in neuronal morphology when the images analyzed have a low signal-to-noise ratio (SNR). This inadequacy derives from the fact that these methods often include data from non-neuronal structures or artifacts by simply tracing pixels with high intensity. In this paper, we describe Neuron Image Analyzer (NIA), a novel algorithm that overcomes these inadequacies by employing Laplacian of Gaussian filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to specifically extract relational pixel information corresponding to neuronal structures (i.e., soma, neurite). As such, NIA that is based on vector representation is less likely to detect false signals (i.e., non-neuronal structures) or generate artifact signals (i.e., deformation of original structures) than current image analysis algorithms that are based on raster representation. We demonstrate that NIA enables precise quantification of neuronal processes (e.g., length and orientation of neurites) in low quality images with a significant increase in the accuracy of detecting neuronal changes post-stimulation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.