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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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The tracing result (green) of an CF image stack overlaps with a slice of the original stack. The result is translated a little from its original position for better view
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Fig8: The tracing result (green) of an CF image stack overlaps with a slice of the original stack. The result is translated a little from its original position for better view

Mentions: We tested the algorithm on 4 of the 5 data sets2 from the Digital Reconstruction of Axonal and Dentritic Morphology (DIADEM) competition (http://www.diademchallenge.org). Named after the stained neuron types, the 4 datasets are CF (Cerebellar Climbing Fibers) (Sugihara et al. 1999), HC (Hippocampal CA3 Interneuron) (Calixto et al. 2008), NL (Neocortical Layer6 Axons) (De Paola et al. 2006) and OP (Olfactory Projection Fibers) (Jefferis et al. 2007). The imaging protocol for each dataset is listed briefly in Table 1. The images of the datasets CF and HC are from bright-field microscopy and some special preprocessing steps, to be described, are necessary before applying model fitting on them. For the NL dataset, an image contains multiple neurons. Given the coordinates of a point on each neuron, we built a single tree first and then cut the edges with largest connection cost to get multiple trees. Also the crossover prior described in the previous section was applied to the NL data only. The final results on all 4 datasets are illustrated in Fig. 8 (CF), Fig. 9 (HC), Fig. 10 (NL), Fig. 11 (OP).Table 1


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)

The tracing result (green) of an CF image stack overlaps with a slice of the original stack. The result is translated a little from its original position for better view
© Copyright Policy
Related In: Results  -  Collection

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

Fig8: The tracing result (green) of an CF image stack overlaps with a slice of the original stack. The result is translated a little from its original position for better view
Mentions: We tested the algorithm on 4 of the 5 data sets2 from the Digital Reconstruction of Axonal and Dentritic Morphology (DIADEM) competition (http://www.diademchallenge.org). Named after the stained neuron types, the 4 datasets are CF (Cerebellar Climbing Fibers) (Sugihara et al. 1999), HC (Hippocampal CA3 Interneuron) (Calixto et al. 2008), NL (Neocortical Layer6 Axons) (De Paola et al. 2006) and OP (Olfactory Projection Fibers) (Jefferis et al. 2007). The imaging protocol for each dataset is listed briefly in Table 1. The images of the datasets CF and HC are from bright-field microscopy and some special preprocessing steps, to be described, are necessary before applying model fitting on them. For the NL dataset, an image contains multiple neurons. Given the coordinates of a point on each neuron, we built a single tree first and then cut the edges with largest connection cost to get multiple trees. Also the crossover prior described in the previous section was applied to the NL data only. The final results on all 4 datasets are illustrated in Fig. 8 (CF), Fig. 9 (HC), Fig. 10 (NL), Fig. 11 (OP).Table 1

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