ArticleJournal of neuroscience methods2009
Detection of the optimal neuron traces in confocal microscopy images.
Article in Journal of neuroscience methods, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 52 citations in OpenAlex.
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- 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
- Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi-Dirac Correction Functions.Entropy (Basel, Switzerland) · 2021Article
- New Features for Neuron Classification.Neuroinformatics · 2019Article
- Optimization of Traced Neuron Skeleton Using Lasso-Based Model.Frontiers in neuroanatomy · 2019Article
- Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.Scientific reports · 2018Article
- Automated Neuron Tracing Methods: An Updated Account.Neuroinformatics · 2016Review
- Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons.Neuroinformatics · 2016Article
- Neuron anatomy structure reconstruction based on a sliding filter.BMC bioinformatics · 2015Article
- Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models.Neuroinformatics · 2015Article
- Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors.Bioinformatics (Oxford, England) · 2015Article
- Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density.Journal of the Royal Society, Interface · 2014Article
- Active learning of neuron morphology for accurate automated tracing of neurites.Frontiers in neuroanatomy · 2014Article
- A pipeline for neuron reconstruction based on spatial sliding volume filter seeding.Computational and mathematical methods in medicine · 2014Article
- Article
- Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.PloS one · 2013Article
- Automatic reconstruction of neural morphologies with multi-scale tracking.Frontiers in neural circuits · 2012Article
- Principal curves as skeletons of tubular objects: locally characterizing the structures of axons.Neuroinformatics · 2011Article
- Neuronal tracing for connectomic studies.Neuroinformatics · 2011Review
- Automated reconstruction of neuronal morphology based on local geometrical and global structural models.Neuroinformatics · 2011Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Quantitative methods of analysis of neural circuits rely on large datasets of neurons reconstructed accurately in three dimensions (3D). Due to the complexity of neuronal arbors, large datasets of reconstructed neurons must be generated with automated algorithms. Here, we attempted to automate the process of neuron tracing from sparsely labeled 3D stacks of confocal microscopy images. Our algorithm involves two steps. In the first step, the segmented image of neurites in the stack is voxel-coded. Centers of intensity of consecutively coded wave fronts are connected into a branched structure, which represents a coarse trace of the neurites. In the second step, this trace is optimized with the modified active contour method, which tends to maximize the intensity along the trace while keeping it under tension. To assess the performance of the algorithm we used manual reconstructions of neurons and converted them into artificial stacks of intensity images. These images were traced using the developed algorithm and quantitatively compared to the corresponding manual traces. The optimal traces were on average 6.0% shorter than the manual traces. This reduction in length resulted from the smoothness of the optimal traces, which, in comparison to the manual ones, were built out of shorter segments, and, as a result, were 3.3% less tortuous. The average distance between the optimal and the manual traces was 0.14 microm, and the average distance between their corresponding branch-points was 2.2 microm, illustrating good agreement between the traces.
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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.