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Automated reconstruction of neuronal morphology based on local geometrical and global structural models.

Zhao T, Xie J, Amat F, Clack N, Ahammad P, Peng H, Long F, Myers E - Neuroinformatics (2011)

Bottom Line: Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience.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.

View Article: PubMed Central - PubMed

Affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, 38 ZheDa Road, Hangzhou 310027, China. tingzhao@gmail.com

ABSTRACT
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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Graph of neurites. A neurite is modeled as a 3-part node a, which can form end-to-end connection b or end-to-body connection c with another node. Real examples of end-to-end connection and end-to-body connection are shown in d and e respectively. Colors indicate different neurites
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Fig4: Graph of neurites. A neurite is modeled as a 3-part node a, which can form end-to-end connection b or end-to-body connection c with another node. Real examples of end-to-end connection and end-to-body connection are shown in d and e respectively. Colors indicate different neurites

Mentions: Suppose we have a set of neurite fibers, . The goal is to determine how they are connected. We first create a neurite graph, in wich each node is a neurite and each edge indicates a possible connection between two neurites. In contrast to a usual graph, a node in a neurite graph has three parts, two ends and a body. So an edge between two nodes in this undirected graph must also specify at each node whether it connects to an end or the body. The connection pattern of an edge can be either end-to-end or end-to-body (Fig. 4), but never body-to-body. Moreover, only one edge is allowed between two neurites. Our problem, in the framework of this graph, is to find a minimum weight spanning tree in the case one neuron is under view and all the Nk are true positives. The whole procedure is illustrated in Fig. 5. In our method, we do not consider the connection between a pair of neurites that are too far away from each other. The distance threshold is set to 20 pixels, which is twice as long as the height of the cylinder templates. Any two neurites that have a distance larger than the threshold are supposed to be from different neurons.Fig. 4


Automated reconstruction of neuronal morphology based on local geometrical and global structural models.

Zhao T, Xie J, Amat F, Clack N, Ahammad P, Peng H, Long F, Myers E - Neuroinformatics (2011)

Graph of neurites. A neurite is modeled as a 3-part node a, which can form end-to-end connection b or end-to-body connection c with another node. Real examples of end-to-end connection and end-to-body connection are shown in d and e respectively. Colors indicate different neurites
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3104133&req=5

Fig4: Graph of neurites. A neurite is modeled as a 3-part node a, which can form end-to-end connection b or end-to-body connection c with another node. Real examples of end-to-end connection and end-to-body connection are shown in d and e respectively. Colors indicate different neurites
Mentions: Suppose we have a set of neurite fibers, . The goal is to determine how they are connected. We first create a neurite graph, in wich each node is a neurite and each edge indicates a possible connection between two neurites. In contrast to a usual graph, a node in a neurite graph has three parts, two ends and a body. So an edge between two nodes in this undirected graph must also specify at each node whether it connects to an end or the body. The connection pattern of an edge can be either end-to-end or end-to-body (Fig. 4), but never body-to-body. Moreover, only one edge is allowed between two neurites. Our problem, in the framework of this graph, is to find a minimum weight spanning tree in the case one neuron is under view and all the Nk are true positives. The whole procedure is illustrated in Fig. 5. In our method, we do not consider the connection between a pair of neurites that are too far away from each other. The distance threshold is set to 20 pixels, which is twice as long as the height of the cylinder templates. Any two neurites that have a distance larger than the threshold are supposed to be from different neurons.Fig. 4

Bottom Line: Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience.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.

View Article: PubMed Central - PubMed

Affiliation: Qiushi Academy for Advanced Studies, Zhejiang University, 38 ZheDa Road, Hangzhou 310027, China. tingzhao@gmail.com

ABSTRACT
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.

Show MeSH