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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.

Show MeSH
Crossover a in a neurite graph b. The solution c, d gives the minimal sum of the angles between matched neurites. The red and green lines in c and d show how the neurites are merged after matching
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Fig7: Crossover a in a neurite graph b. The solution c, d gives the minimal sum of the angles between matched neurites. The red and green lines in c and d show how the neurites are merged after matching

Mentions: When one fiber passes another very closely, especially in the axial z-dimension where resolution is lower with most microscopes, their signal may overlap to the extent that the geodesic cost function (falsely) indicates that they should be joined (Fig. 6). In other words, the overlap pattern cannot be identified by the MST algorithm. We call such a pattern a crossover, and it must be distinguished from the real fusions that need to occur at branch points in a neuron. In the neurite graph, a crossover has one of two special signatures depending on whether the fiber tracing passes through the crossover region or not. If the tracing does not pass, the pattern will be pairwise end-to-end connections among four or more nodes (Fig. 7). Otherwise, the pattern will be the connections from the ends of two or more neurites to the body of another neurite (imagine fibers 1 and 3 are already joined into a single fiber in Fig. 7). For the latter case, the problem is compounded as the local tracing that went through the crossover region may have “jumped tracks” to another fiber. So conservatively, we break any fibers that pass through the connection region. This has the further advantage of reducing the problem to the first case where all possible connections are end-to-end.Fig. 6


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)

Crossover a in a neurite graph b. The solution c, d gives the minimal sum of the angles between matched neurites. The red and green lines in c and d show how the neurites are merged after matching
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3104133&req=5

Fig7: Crossover a in a neurite graph b. The solution c, d gives the minimal sum of the angles between matched neurites. The red and green lines in c and d show how the neurites are merged after matching
Mentions: When one fiber passes another very closely, especially in the axial z-dimension where resolution is lower with most microscopes, their signal may overlap to the extent that the geodesic cost function (falsely) indicates that they should be joined (Fig. 6). In other words, the overlap pattern cannot be identified by the MST algorithm. We call such a pattern a crossover, and it must be distinguished from the real fusions that need to occur at branch points in a neuron. In the neurite graph, a crossover has one of two special signatures depending on whether the fiber tracing passes through the crossover region or not. If the tracing does not pass, the pattern will be pairwise end-to-end connections among four or more nodes (Fig. 7). Otherwise, the pattern will be the connections from the ends of two or more neurites to the body of another neurite (imagine fibers 1 and 3 are already joined into a single fiber in Fig. 7). For the latter case, the problem is compounded as the local tracing that went through the crossover region may have “jumped tracks” to another fiber. So conservatively, we break any fibers that pass through the connection region. This has the further advantage of reducing the problem to the first case where all possible connections are end-to-end.Fig. 6

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