Reconstruction and simulation for three-dimensional morphological structure of insect neurons

Yamasaki, Takayuki; Isokawa, Teijiro; Matsui, Nobuyuki; Ikeno, Hidetoshi; Kanzaki, Ryohei

doi:10.1016/j.neucom.2005.12.042

Cited by 19 publications

(19 citation statements)

References 6 publications

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“…A number of neuroscientists have recently focused their attention to insect brains as such targets [1], [2], [3]. In [4], a method for reconstructing threedimensional morphological structures from slice images of interneurons in silkworm moth brains is presented, and electrical response properties of reconstructed interneurons are analyzed. Morphology-based classification is considered to be useful in further promoting comprehensive analysis from sensory reception to behavior.…”

Section: Introductionmentioning

confidence: 99%

On Self-Organizing Map Based Classification of Insect Neurons

Urata

Ohtsuka

Isokawa

et al. 2006

TENCON 2006 - 2006 IEEE Region 10 Conference

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In this paper, a systematic method based on selforganizing maps is presented to classify interneurons of silkworm moths. Denseness of branching structures and existence of thick main dendrites are quantified by six fractal dimension values and three values calculated from images to which fundamental processing techniques are applied, respectively. Such values are employed as nine elements in training data for a map. The classification result is obtained as clusters with units in the trained map. Experimental results establish that the classification executed by the proposed method is comparable in accuracy to the manually executed classification.

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Section: Introductionmentioning

confidence: 99%

On Self-Organizing Map Based Classification of Insect Neurons

Urata

Ohtsuka

Isokawa

et al. 2006

TENCON 2006 - 2006 IEEE Region 10 Conference

Self Cite

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“…A simple way to obtain the topology of a 3D segmented structure and to examine its connectedness is the voxel-coding algorithm (Zhou et al, 1998; Zhou and Toga, 1999; Shikata et al, 2004; Yamasaki et al, 2006). In this recursive algorithm, a label (a natural number) is associated with each voxel of the segmented image (Figure 2A).…”

Section: Resultsmentioning

confidence: 99%

“…The automated tracing algorithm presented in this study consists of two parts. First, we voxel-coded the segmented intensity image (Zhou et al, 1998; Zhou and Toga, 1999; Shikata et al, 2004; Yamasaki et al, 2006) and extracted the topology of the 3D neuronal structure. Second, to find the optimal trace of the segmented neurites, we smoothed and optimized this structure by using a modified active contour method (Kass et al, 1988; Frangi et al, 1999; Schmitt et al, 2004).…”

Section: Discussionmentioning

confidence: 99%

Detection of the optimal neuron traces in confocal microscopy images

Vasilkoski

Stepanyants

2009

Journal of Neuroscience Methods

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Quantitative methods of analysis of neural circuits rely on large datasets of neurons reconstructed accurately in three dimensions (3D). Due to the complexity of neuronal arbors, large datasets of reconstructed neurons must be generated with automated algorithms. Here, we attempted to automate the process of neuron tracing from sparsely labeled 3D stacks of confocal microscopy images. Our algorithm involves two steps. In the first step, the segmented image of neurites in the stack is voxelcoded. Centers of intensity of consecutively coded wave fronts are connected into a branched structure, which represents a coarse trace of the neurites. In the second step, this trace is optimized with the modified active contour method, which tends to maximize the intensity along the trace while keeping it under tension. To assess the performance of the algorithm we used manual reconstructions of neurons and converted them into artificial stacks of intensity images. These images were traced using the developed algorithm and quantitatively compared to the corresponding manual traces. The optimal traces were on average 6.0% shorter than the manual traces. This reduction in length resulted from the smoothness of the optimal traces, which, in comparison to the manual ones, were built out of shorter segments, and, as a result, were 3.3% less tortuous. The average distance between the optimal and the manual traces was 0.14 μm, and the average distance between their corresponding branch-points was 2.2 μm, illustrating good agreement between the traces.

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“…In addition to these software packages, several publications (Schmitt et al, 2004;Evers et al, 2005;Yamasaki et al, 2006;Oberlaender et al, 2007) described research efforts to automate neuron reconstruction, which, however, did not lead to distributed software. Other automation methods previously explored include intensity-thresholding methods (Sahoo et al, 1988;Holmes, 2002;Koh et al, 2002), which are faster but generally highly susceptible to background noise.…”

Section: Methods For Neuron Tracing and Reconstructionmentioning

confidence: 99%