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dc.contributor.authorLu, Juen_US
dc.contributor.authorFiala, John C.en_US
dc.contributor.authorLichtman, Jeff W.en_US
dc.date.accessioned2012-01-11T22:26:45Z
dc.date.available2012-01-11T22:26:45Z
dc.date.issued2009-5-21
dc.identifier.citationLu, Ju, John C. Fiala, Jeff W. Lichtman. "Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images" PLoS ONE 4(5): e5655. (2009)
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/2144/3294
dc.description.abstractWe introduce a method for large scale reconstruction of complex bundles of neural processes from fluorescent image stacks. We imaged yellow fluorescent protein labeled axons that innervated a whole muscle, as well as dendrites in cerebral cortex, in transgenic mice, at the diffraction limit with a confocal microscope. Each image stack was digitally re-sampled along an orientation such that the majority of axons appeared in cross-section. A region growing algorithm was implemented in the open-source Reconstruct software and applied to the semi-automatic tracing of individual axons in three dimensions. The progression of region growing is constrained by user-specified criteria based on pixel values and object sizes, and the user has full control over the segmentation process. A full montage of reconstructed axons was assembled from the ~200 individually reconstructed stacks. Average reconstruction speed is ~0.5 mm per hour. We found an error rate in the automatic tracing mode of ~1 error per 250 um of axonal length. We demonstrated the capacity of the program by reconstructing the connectome of motor axons in a small mouse muscle.en_US
dc.description.sponsorshipNational Institutes of Health; Microsoft Research; Fu Fellowshipen_US
dc.language.isoen
dc.publisherPublic Library of Scienceen_US
dc.titleSemi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Imagesen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0005655
dc.identifier.pmid19479070
dc.identifier.pmcid2682575


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