ArticleNeuroinformatics2015
Improved automatic centerline tracing for dendritic and axonal structures.
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation.Neuroinformatics · 2019Article
- Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer.Scientific reports · 2018Article
- Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks.Neuroinformatics · 2017Article
- Multiscale Analysis of Neurite Orientation and Spatial Organization in Neuronal Images.Neuroinformatics · 2016Article
- Genetic deletion of fibroblast growth factor 14 recapitulates phenotypic alterations underlying cognitive impairment associated with schizophrenia.Translational psychiatry · 2016Article
- Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors.Bioinformatics (Oxford, England) · 2015Article
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Authors and funding
4 authors.
Funding
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Abstract
Centerline tracing in dendritic structures acquired from confocal images of neurons is an essential tool for the construction of geometrical representations of a neuronal network from its coarse scale up to its fine scale structures. In this paper, we propose an algorithm for centerline extraction that is both highly accurate and computationally efficient. The main novelties of the proposed method are (1) the use of a small set of Multiscale Isotropic Laplacian filters, acting as self-steerable filters, for a quick and efficient binary segmentation of dendritic arbors and axons; (2) an automated centerline seed points detection method based on the application of a simple 3D finite-length filter. The performance of this algorithm, which is validated on data from the DIADEM set appears to be very competitive when compared with other state-of-the-art algorithms.
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Registered trials
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