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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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Related in: MedlinePlus

Example of a thick neuron cross-section where the intensity profile is flattened in the center. a is the actual image cross section. b is a plot of the intensity profile of the cross section in a
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Fig2: Example of a thick neuron cross-section where the intensity profile is flattened in the center. a is the actual image cross section. b is a plot of the intensity profile of the cross section in a

Mentions: Of course, the support of the filter must be finite not only for computational purposes, but also because, while the model demands that background surround (most) of the neurite fiber, making this zone too large means that fibers passing very near by will disrupt the fit of the filter. So for a given filter instance, regardless of a and r, we limit the support of the filter to τ pixels beyond the zero-crossing of the Mexican Hat. The second consideration, is that for very thick neurons, their intensity profile often has a flattened top due to saturation of the signal in the center (e.g. Fig. 2). For such a profile a slight deformation of the template induces little or no change in score, so we empirically found that regularizing the template family by multiplying the positive part of the Mexican Hat by (ar2)1/4 solves the problem.Fig. 2


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)

Example of a thick neuron cross-section where the intensity profile is flattened in the center. a is the actual image cross section. b is a plot of the intensity profile of the cross section in a
© Copyright Policy
Related In: Results  -  Collection

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

Fig2: Example of a thick neuron cross-section where the intensity profile is flattened in the center. a is the actual image cross section. b is a plot of the intensity profile of the cross section in a
Mentions: Of course, the support of the filter must be finite not only for computational purposes, but also because, while the model demands that background surround (most) of the neurite fiber, making this zone too large means that fibers passing very near by will disrupt the fit of the filter. So for a given filter instance, regardless of a and r, we limit the support of the filter to τ pixels beyond the zero-crossing of the Mexican Hat. The second consideration, is that for very thick neurons, their intensity profile often has a flattened top due to saturation of the signal in the center (e.g. Fig. 2). For such a profile a slight deformation of the template induces little or no change in score, so we empirically found that regularizing the template family by multiplying the positive part of the Mexican Hat by (ar2)1/4 solves the problem.Fig. 2

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
Related in: MedlinePlus