Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account m...

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Detalles Bibliográficos
Autores principales: Zhao, Ting, Xie, Jun, Amat, Fernando, Clack, Nathan, Ahammad, Parvez, Peng, Hanchuan, Long, Fuhui, Myers, Eugene
Formato: Texto
Lenguaje:English
Publicado: Springer-Verlag 2011
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3104133/
https://www.ncbi.nlm.nih.gov/pubmed/21547564
http://dx.doi.org/10.1007/s12021-011-9120-3
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author Zhao, Ting
Xie, Jun
Amat, Fernando
Clack, Nathan
Ahammad, Parvez
Peng, Hanchuan
Long, Fuhui
Myers, Eugene
author_facet Zhao, Ting
Xie, Jun
Amat, Fernando
Clack, Nathan
Ahammad, Parvez
Peng, Hanchuan
Long, Fuhui
Myers, Eugene
author_sort Zhao, Ting
collection PubMed
description Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.
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spelling pubmed-31041332011-07-14 Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models Zhao, Ting Xie, Jun Amat, Fernando Clack, Nathan Ahammad, Parvez Peng, Hanchuan Long, Fuhui Myers, Eugene Neuroinformatics Original Article Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets. Springer-Verlag 2011-05-06 2011 /pmc/articles/PMC3104133/ /pubmed/21547564 http://dx.doi.org/10.1007/s12021-011-9120-3 Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Original Article
Zhao, Ting
Xie, Jun
Amat, Fernando
Clack, Nathan
Ahammad, Parvez
Peng, Hanchuan
Long, Fuhui
Myers, Eugene
Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models
title Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models
title_full Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models
title_fullStr Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models
title_full_unstemmed Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models
title_short Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models
title_sort automated reconstruction of neuronal morphology based on local geometrical and global structural models
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3104133/
https://www.ncbi.nlm.nih.gov/pubmed/21547564
http://dx.doi.org/10.1007/s12021-011-9120-3
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