Full metadata record
DC FieldValueLanguage
dc.contributor.authorLee, Ping-Changen_US
dc.contributor.authorChuang, Chao-Chunen_US
dc.contributor.authorChiang, Ann-Shynen_US
dc.contributor.authorChing, Yu-Taien_US
dc.date.accessioned2014-12-08T15:28:20Z-
dc.date.available2014-12-08T15:28:20Z-
dc.date.issued2012-09-01en_US
dc.identifier.issn1553-7358en_US
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pcbi.1002658en_US
dc.identifier.urihttp://hdl.handle.net/11536/20494-
dc.description.abstractDrosophila 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.en_US
dc.language.isoen_USen_US
dc.titleHigh-throughput Computer Method for 3D Neuronal Structure Reconstruction from the Image Stack of the Drosophila Brain and Its Applicationsen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pcbi.1002658en_US
dc.identifier.journalPLOS COMPUTATIONAL BIOLOGYen_US
dc.citation.volume8en_US
dc.citation.issue9en_US
dc.citation.epageen_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000309510900006-
dc.citation.woscount5-
Appears in Collections:Articles


Files in This Item:

  1. 000309510900006.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.