Browsing by Author "Lee, Ping-Chang"

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A semi-automatic method for neuron centerline extraction in confocal microscopic image stack
(2008)
Lee, Ping-Chang
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Ching, Yu-Tai
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Chang, H. M.
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Chiang, Ann-Shyn
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資訊工程學系
An algorithm for extracting the centerlines of neurons from 3-D image stack collected from a laser scanning confocal microscope is presented. Recovery of neuronal structure from image stack is critical for quantitative analysis of neuron-morphology. Many methods have been proposed to extract the centerline from the tubular structure in medical images, such as vessels. But the same methods do not work well in processing of neurons. One of the reasons is that the physical limitations of the spatial resolution of the image stack collect by confocal microscopy in the z-direction is much worse than the resolution in x and y directions. In our studied cases the mean voxel size of the image stack is 0.17 x 0.17 x 1.0 mu m(3) i.e., the resolution in z-direction cannot reflect the fact that the neuron has a tubular structure. In this paper, we propose an almost automatic neuron-tracing algorithm for a set of confocal microscopic images of neuron. The method is designed based on finding a minimal path in the volume to extract the centerlines of a neuron.
Computer Aided Alignment and Quantitative 4D Structural Plasticity Analysis of Neurons
(2013-04-01)
Lee, Ping-Chang
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He, Hai-yan
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Lin, Chih-Yang
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Ching, Yu-Tai
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Cline, Hollis T.
The rapid development of microscopic imaging techniques has greatly facilitated time-lapse imaging of neuronal morphology. However, analysis of structural dynamics in the vast amount of 4-Dimensional data generated by in vivo or ex vivo time-lapse imaging still relies heavily on manual comparison, which is not only laborious, but also introduces errors and discrepancies between individual researchers and greatly limits the research pace. Here we present a supervised 4D Structural Plasticity Analysis (4D SPA) computer method to align and match 3-Dimensional neuronal structures across different time points on a semi-automated basis. We demonstrate 2 applications of the method to analyze time-lapse data showing gross morphological changes in dendritic arbor morphology and to identify the distribution and types of branch dynamics seen in a series of time-lapse images. Analysis of the dynamic changes of neuronal structure can be done much faster and with greatly improved consistency and reliability with the 4D SPA supervised computer program. Users can format the neuronal reconstruction data to be used for this analysis. We provide file converters for Neurolucida and Imaris users. The program and user manual are publically accessible and operate through a graphical user interface on Windows and Mac OSX.
Constructing Neuronal Structure from 3D Confocal Microscopic Images
(2009)
Lee, Ping-Chang
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Chang, Hsiu-Ming
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Lin, Chih-Yang
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Chiang, Ann-Shyn
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Ching, Yu-Tai
A semiautomatic method, based on the gradient flow (GVF) snake, to construct the neuronal structure from 3D confocal microscopic images is presented. A single neuron can be labeled by using green fluorescent protein (GFP) such that the 3D neuronal structure can be visualized in a stack of confocal microscopic images. To construct the neuronal structure, we traced a target fiber by providing a rough initial path to approximate that fiber. The path is then deformed under the gradient vector field to converge to the centerline of the fiber by using the GVF snake method. Using our developed software, a neuronal sturctural can be efficiently reconstructed and the tracing results can be reduced to the tree structure of the neuron. Given the tree structure of the neuron, quantitative information can be derived and further analysis can be carried out.
High-throughput Computer Method for 3D Neuronal Structure Reconstruction from the Image Stack of the Drosophila Brain and Its Applications
(2012-09-01)
Lee, Ping-Chang
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Chuang, Chao-Chun
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Chiang, Ann-Shyn
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Ching, Yu-Tai
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生物資訊及系統生物研究所
Drosophila melanogaster is a well-studied model organism, especially in the field of neurophysiology and neural circuits. The brain of the Drosophila is small but complex, and the image of a single neuron in the brain can be acquired using confocal microscopy. Analyzing the Drosophila brain is an ideal start to understanding the neural structure. The most fundamental task in studying the neural network of Drosophila is to reconstruct neuronal structures from image stacks. Although the fruit fly brain is small, it contains approximately 100 000 neurons. It is impossible to trace all the neurons manually. This study presents a high-throughput algorithm for reconstructing the neuronal structures from 3D image stacks collected by a laser scanning confocal microscope. The proposed method reconstructs the neuronal structure by applying the shortest path graph algorithm. The vertices in the graph are certain points on the 2D skeletons of the neuron in the slices. These points are close to the 3D centerlines of the neuron branches. The accuracy of the algorithm was verified using the DIADEM data set. This method has been adopted as part of the protocol of the FlyCircuit Database, and was successfully applied to process more than 16 000 neurons. This study also shows that further analysis based on the reconstruction results can be performed to gather more information on the neural network.
Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution
(2011-01-11)
Chiang, Ann-Shyn
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Lin, Chih-Yung
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Chuang, Chao-Chun
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Chang, Hsiu-Ming
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Hsieh, Chang-Huain
Background: Animal behavior is governed by the activity of interconnected brain circuits. Comprehensive brain wiring maps are thus needed in order to formulate hypotheses about information flow and also to guide genetic manipulations aimed at understanding how genes and circuits orchestrate complex behaviors. Results: To assemble this map, we deconstructed the adult Drosophila brain into approximately 16,000. single neurons and reconstructed them into a common standardized framework to produce a virtual fly brain. We have constructed a mesoscopic map and found that it consists of 41 local processing units (LPUs), six hubs, and 58 tracts covering the whole Drosophila brain. Despite individual local variation, the architecture of the Drosophila brain shows invariance for both the aggregation of local neurons (LNs) within specific LPUs and for the connectivity of projection neurons (PNs) between the same set of LPUs. An open-access image database, named FlyCircuit, has been constructed for online data archiving, mining, analysis, and three-dimensional visualization of all single neurons, brain-wide LPUs, their wiring diagrams, and neural tracts. Conclusion: We found that the Drosophila brain is assembled from families of multiple LPUs and their interconnections. This provides an essential first step in the analysis of information processing within and between neurons in a complete brain.