ArticleFrontiers in neural circuits2012
Automatic reconstruction of neural morphologies with multi-scale tracking.
Article in Frontiers in neural circuits, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain.eLife · 2025Article
- Smart imaging to empower brain-wide neuroscience at single-cell levels.Brain informatics · 2022Review
- Hidden Markov modeling for maximum probability neuron reconstruction.Communications biology · 2022Article
- Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites.Neuroinformatics · 2019Article
- Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation.Neuroinformatics · 2019Article
- Graph-Based Models of Cortical Axons for the Prediction of Neuronal Response to Extracellular Electrical Stimulation.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2018Article
- DeepNeuron: an open deep learning toolbox for neuron tracing.Brain informatics · 2018Article
- Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing.Magnetic resonance in medicine · 2018Article
- Ensemble Neuron Tracer for 3D Neuron Reconstruction.Neuroinformatics · 2017Article
- 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
- Adaptive Image Enhancement for Tracing 3D Morphologies of Neurons and Brain Vasculatures.Neuroinformatics · 2015Article
- Modeling brain circuitry over a wide range of scales.Frontiers in neuroanatomy · 2015Review
- Automated computation of arbor densities: a step toward identifying neuronal cell types.Frontiers in neuroanatomy · 2014Article
- Active learning of neuron morphology for accurate automated tracing of neurites.Frontiers in neuroanatomy · 2014Article
- APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree.Bioinformatics (Oxford, England) · 2013Article
- Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.PloS one · 2013Article
- NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model.Scientific reports · 2013Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Neurons have complex axonal and dendritic morphologies that are the structural building blocks of neural circuits. The traditional method to capture these morphological structures using manual reconstructions is time-consuming and partly subjective, so it appears important to develop automatic or semi-automatic methods to reconstruct neurons. Here we introduce a fast algorithm for tracking neural morphologies in 3D with simultaneous detection of branching processes. The method is based on existing tracking procedures, adding the machine vision technique of multi-scaling. Starting from a seed point, our algorithm tracks axonal or dendritic arbors within a sphere of a variable radius, then moves the sphere center to the point on its surface with the shortest Dijkstra path, detects branching points on the surface of the sphere, scales it until branches are well separated and then continues tracking each branch. We evaluate the performance of our algorithm on preprocessed data stacks obtained by manual reconstructions of neural cells, corrupted with different levels of artificial noise, and unprocessed data sets, achieving 90% precision and 81% recall in branch detection. We also discuss limitations of our method, such as reconstructing highly overlapping neural processes, and suggest possible improvements. Multi-scaling techniques, well suited to detect branching structures, appear a promising strategy for automatic neuronal reconstructions.
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