標題: High-throughput Computer Method for 3D Neuronal Structure Reconstruction from the Image Stack of the Drosophila Brain and Its Applications
作者: Lee, Ping-Chang
Chuang, Chao-Chun
Chiang, Ann-Shyn
Ching, Yu-Tai
生物資訊及系統生物研究所
資訊工程學系
Institude of Bioinformatics and Systems Biology
Department of Computer Science
公開日期: 1-九月-2012
摘要: 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.
URI: http://dx.doi.org/10.1371/journal.pcbi.1002658
http://hdl.handle.net/11536/20494
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1002658
期刊: PLOS COMPUTATIONAL BIOLOGY
Volume: 8
Issue: 9
結束頁: 
顯示於類別:期刊論文


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  1. 000309510900006.pdf