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Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images

We 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 c...

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Detalles Bibliográficos
Autores principales: Lu, Ju, Fiala, John C., Lichtman, Jeff W.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682575/
https://www.ncbi.nlm.nih.gov/pubmed/19479070
http://dx.doi.org/10.1371/journal.pone.0005655
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author Lu, Ju
Fiala, John C.
Lichtman, Jeff W.
author_facet Lu, Ju
Fiala, John C.
Lichtman, Jeff W.
author_sort Lu, Ju
collection PubMed
description We 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.
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spelling pubmed-26825752009-05-27 Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images Lu, Ju Fiala, John C. Lichtman, Jeff W. PLoS One Research Article We 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. Public Library of Science 2009-05-21 /pmc/articles/PMC2682575/ /pubmed/19479070 http://dx.doi.org/10.1371/journal.pone.0005655 Text en Lu et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Lu, Ju
Fiala, John C.
Lichtman, Jeff W.
Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images
title Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images
title_full Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images
title_fullStr Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images
title_full_unstemmed Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images
title_short Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images
title_sort semi-automated reconstruction of neural processes from large numbers of fluorescence images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682575/
https://www.ncbi.nlm.nih.gov/pubmed/19479070
http://dx.doi.org/10.1371/journal.pone.0005655
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