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Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling
by
Ding, Wenxiang
, Li, Anan
, Wu, Jingpeng
, Liu, Qian
, Zeng, Shaoqun
, Yan, Cheng
, Ming, Xing
, Gong, Hui
in
Algorithms
/ Animals
/ Automation
/ Binary systems
/ Biology
/ Biomedical engineering
/ Brain
/ Brain - anatomy & histology
/ Circuits
/ Computer Science
/ Digital imaging
/ Engineering
/ Image acquisition
/ Image analysis
/ Image processing
/ Image reconstruction
/ Imaging, Three-Dimensional - methods
/ Interactive systems
/ Iterative methods
/ Laboratories
/ Light
/ Light microscopy
/ Medical imaging
/ Mice
/ Microscopy
/ Microscopy - methods
/ Morphology
/ Neural circuitry
/ Neural networks
/ Neural Pathways - anatomy & histology
/ Neurons
/ Neurons - cytology
/ Neurosciences
/ Optical microscopy
/ Optical sectioning
/ Orthogonality
/ Photonics
/ Sampling
/ Science
/ Sectioning
/ Software
/ Source code
/ Synthetic data
2013
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Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling
by
Ding, Wenxiang
, Li, Anan
, Wu, Jingpeng
, Liu, Qian
, Zeng, Shaoqun
, Yan, Cheng
, Ming, Xing
, Gong, Hui
in
Algorithms
/ Animals
/ Automation
/ Binary systems
/ Biology
/ Biomedical engineering
/ Brain
/ Brain - anatomy & histology
/ Circuits
/ Computer Science
/ Digital imaging
/ Engineering
/ Image acquisition
/ Image analysis
/ Image processing
/ Image reconstruction
/ Imaging, Three-Dimensional - methods
/ Interactive systems
/ Iterative methods
/ Laboratories
/ Light
/ Light microscopy
/ Medical imaging
/ Mice
/ Microscopy
/ Microscopy - methods
/ Morphology
/ Neural circuitry
/ Neural networks
/ Neural Pathways - anatomy & histology
/ Neurons
/ Neurons - cytology
/ Neurosciences
/ Optical microscopy
/ Optical sectioning
/ Orthogonality
/ Photonics
/ Sampling
/ Science
/ Sectioning
/ Software
/ Source code
/ Synthetic data
2013
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Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling
by
Ding, Wenxiang
, Li, Anan
, Wu, Jingpeng
, Liu, Qian
, Zeng, Shaoqun
, Yan, Cheng
, Ming, Xing
, Gong, Hui
in
Algorithms
/ Animals
/ Automation
/ Binary systems
/ Biology
/ Biomedical engineering
/ Brain
/ Brain - anatomy & histology
/ Circuits
/ Computer Science
/ Digital imaging
/ Engineering
/ Image acquisition
/ Image analysis
/ Image processing
/ Image reconstruction
/ Imaging, Three-Dimensional - methods
/ Interactive systems
/ Iterative methods
/ Laboratories
/ Light
/ Light microscopy
/ Medical imaging
/ Mice
/ Microscopy
/ Microscopy - methods
/ Morphology
/ Neural circuitry
/ Neural networks
/ Neural Pathways - anatomy & histology
/ Neurons
/ Neurons - cytology
/ Neurosciences
/ Optical microscopy
/ Optical sectioning
/ Orthogonality
/ Photonics
/ Sampling
/ Science
/ Sectioning
/ Software
/ Source code
/ Synthetic data
2013
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Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling
Journal Article
Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling
2013
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Overview
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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