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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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Preprocessing result for the HC dataset: a Original image; b Processed image
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Fig15: Preprocessing result for the HC dataset: a Original image; b Processed image

Mentions: Preprocessing for the HC Dataset Much of the preprocessing required for this dataset was due to either poor microscopy or poor stitching of the multiple 3D stacks that made up each data set. In particular, the z-planes of a given tile were offset with respect to each other and the luminence of each plane varied significantly. We do not know how these artifacts were introduced, but we perforce had to correct them.First the images were converted to gray-scale for the neuron intensity using the color model scheme used for CF, save that here matters were particular easy as there was only one material. Next the motion artifacts were corrected by splitting the full volume into a series of subvolumes corresponding to the lateral tiling used to originally acquire the data. Each subvolume was then processed independently. For each pair of successive z-planes, a lateral translation was estimated as that maximizing the correlation between each edge-enhanced representations of the respective planes. Edge enhanced images were computed by filtering with a difference-of-boxes filter (box sizes 5 and 15 pixels) followed by masking with the original data binarized according to the Ostu threshold. Linear interpolation was used to align images according to the estimated translation. Border regions were filled by clamping to the edges. After motion correction, the volume was median filtered along z (window size 1 ×1 ×13) in order to correct for intensity fluctuations between planes. The window size was chosen to approximate the observed extent of a single neurite image along the z direction. Finally, the vertical blur from strongly labeled objects, such as the cell soma, added an approximate constant signal to all planes above and below the object. To remove this signal, we estimated it as the median plane along z and subtracted that from each plane clamping negative values to zero. An example of processing result is shown in Fig. 15.Fig. 15


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)

Preprocessing result for the HC dataset: a Original image; b Processed image
© Copyright Policy
Related In: Results  -  Collection

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

Fig15: Preprocessing result for the HC dataset: a Original image; b Processed image
Mentions: Preprocessing for the HC Dataset Much of the preprocessing required for this dataset was due to either poor microscopy or poor stitching of the multiple 3D stacks that made up each data set. In particular, the z-planes of a given tile were offset with respect to each other and the luminence of each plane varied significantly. We do not know how these artifacts were introduced, but we perforce had to correct them.First the images were converted to gray-scale for the neuron intensity using the color model scheme used for CF, save that here matters were particular easy as there was only one material. Next the motion artifacts were corrected by splitting the full volume into a series of subvolumes corresponding to the lateral tiling used to originally acquire the data. Each subvolume was then processed independently. For each pair of successive z-planes, a lateral translation was estimated as that maximizing the correlation between each edge-enhanced representations of the respective planes. Edge enhanced images were computed by filtering with a difference-of-boxes filter (box sizes 5 and 15 pixels) followed by masking with the original data binarized according to the Ostu threshold. Linear interpolation was used to align images according to the estimated translation. Border regions were filled by clamping to the edges. After motion correction, the volume was median filtered along z (window size 1 ×1 ×13) in order to correct for intensity fluctuations between planes. The window size was chosen to approximate the observed extent of a single neurite image along the z direction. Finally, the vertical blur from strongly labeled objects, such as the cell soma, added an approximate constant signal to all planes above and below the object. To remove this signal, we estimated it as the median plane along z and subtracted that from each plane clamping negative values to zero. An example of processing result is shown in Fig. 15.Fig. 15

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