ArticlePloS one2013
Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- BEA-CACE: branch-endpoint-aware double-DQN for coronary artery centerline extraction in CT angiography images.International journal of computer assisted radiology and surgery · 2025Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- Complete Neuron Reconstruction Based on Branch Confidence.Brain sciences · 2024Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Preclinical Western Blot in the Era of Digital Transformation and Reproducible Research, an Eastern Perspective.Interdisciplinary sciences, computational life sciences · 2021Article
- Exploring highly reliable substructures in auto-reconstructions of a neuron.Brain informatics · 2021Article
- Retrieving similar substructures on 3D neuron reconstructions.Brain informatics · 2020Article
- A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets.Frontiers in neuroinformatics · 2020Article
- Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows.Frontiers in neuroanatomy · 2020Article
- Application of mild hypothermia successfully mitigates neural injury in a 3D in-vitro model of traumatic brain injury.PloS one · 2020Article
- Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation.Neuroinformatics · 2019Article
- Gotta Trace 'em All: A Mini-Review on Tools and Procedures for Segmenting Single Neurons Toward Deciphering the Structural Connectome.Frontiers in bioengineering and biotechnology · 2019Review
- Automated 3D Soma Segmentation with Morphological Surface Evolution for Neuron Reconstruction.Neuroinformatics · 2018Article
- Touching Soma Segmentation Based on the Rayburst Sampling Algorithm.Neuroinformatics · 2017Article
- Ensemble Neuron Tracer for 3D Neuron Reconstruction.Neuroinformatics · 2017Article
- SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images.Neuroinformatics · 2017Article
- Precise Cerebral Vascular Atlas in Stereotaxic Coordinates of Whole Mouse Brain.Frontiers in neuroanatomy · 2017Article
- Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking.Neuroinformatics · 2016Article
- Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images.Neuroinformatics · 2016Article
- TReMAP: Automatic 3D Neuron Reconstruction Based on Tracing, Reverse Mapping and Assembling of 2D Projections.Neuroinformatics · 2016Article
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Authors and funding
8 authors.
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Abstract
Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.
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