Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.

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 prese...

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Autores principales:	Xing Ming, Anan Li, Jingpeng Wu, Cheng Yan, Wenxiang Ding, Hui Gong, Shaoqun Zeng, Qian Liu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c8582f951b99498db35cb113358e3ef2
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id	oai:doaj.org-article:c8582f951b99498db35cb113358e3ef2
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spelling	oai:doaj.org-article:c8582f951b99498db35cb113358e3ef22021-11-18T08:39:18ZRapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.1932-620310.1371/journal.pone.0084557https://doaj.org/article/c8582f951b99498db35cb113358e3ef22013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24391966/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Digital 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.Xing MingAnan LiJingpeng WuCheng YanWenxiang DingHui GongShaoqun ZengQian LiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e84557 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xing Ming Anan Li Jingpeng Wu Cheng Yan Wenxiang Ding Hui Gong Shaoqun Zeng Qian Liu Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.
description	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.
format	article
author	Xing Ming Anan Li Jingpeng Wu Cheng Yan Wenxiang Ding Hui Gong Shaoqun Zeng Qian Liu
author_facet	Xing Ming Anan Li Jingpeng Wu Cheng Yan Wenxiang Ding Hui Gong Shaoqun Zeng Qian Liu
author_sort	Xing Ming
title	Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.
title_short	Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.
title_full	Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.
title_fullStr	Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.
title_full_unstemmed	Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.
title_sort	rapid reconstruction of 3d neuronal morphology from light microscopy images with augmented rayburst sampling.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c8582f951b99498db35cb113358e3ef2
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_version_	1718421527177723904

Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling.

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