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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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Scenarios causing problems for our tracing method: a The two branch tips are so close that the tracing jumps from one to the other; b The cylinder model may fail to fit on a short branch between two close branch points; c Two examples of broken neurite signal on which the tracing will stop too early at any seeding point
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Fig17: Scenarios causing problems for our tracing method: a The two branch tips are so close that the tracing jumps from one to the other; b The cylinder model may fail to fit on a short branch between two close branch points; c Two examples of broken neurite signal on which the tracing will stop too early at any seeding point

Mentions: The positive feature of our template model is that given sufficient support h it clearly distinguishes neurite from non-neurite artifacts. That is it has a low false positive rate of identification (an example is shown in Fig. 16). The problem is that it does not detect short features that are at fewer than h pixels in size such as short side branches, or sometimes it will erroneously fit the model over a small break that actually separates two fibers. To further improve the method, its limitation in these circumstances must be addressed (Fig. 17) and we suggest approaches as follows:Fig. 16


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)

Scenarios causing problems for our tracing method: a The two branch tips are so close that the tracing jumps from one to the other; b The cylinder model may fail to fit on a short branch between two close branch points; c Two examples of broken neurite signal on which the tracing will stop too early at any seeding point
© Copyright Policy
Related In: Results  -  Collection

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

Fig17: Scenarios causing problems for our tracing method: a The two branch tips are so close that the tracing jumps from one to the other; b The cylinder model may fail to fit on a short branch between two close branch points; c Two examples of broken neurite signal on which the tracing will stop too early at any seeding point
Mentions: The positive feature of our template model is that given sufficient support h it clearly distinguishes neurite from non-neurite artifacts. That is it has a low false positive rate of identification (an example is shown in Fig. 16). The problem is that it does not detect short features that are at fewer than h pixels in size such as short side branches, or sometimes it will erroneously fit the model over a small break that actually separates two fibers. To further improve the method, its limitation in these circumstances must be addressed (Fig. 17) and we suggest approaches as follows:Fig. 16

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