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  Contour-propagation algorithms for semi-automated reconstruction of neural processes

Macke, J. H., Maack, N., Gupta, R., Denk, W., Schölkopf, B., & Borst, A. (2008). Contour-propagation algorithms for semi-automated reconstruction of neural processes. Journal of Neuroscience Methods, 167(2), 349-357. doi:10.1016/j.jneumeth.2007.07.021.

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 Creators:
Macke, Jakob H1, Author           
Maack, N.2, Author           
Gupta, R., Author
Denk, Winfried3, Author           
Schölkopf, Bernhard1, Author           
Borst, A.2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department: Systems and Computational Neurobiology / Borst, MPI of Neurobiology, Max Planck Society, ou_1113548              
3Department of Biomedical Optics, Max Planck Institute for Medical Research, Max Planck Society, ou_1497699              

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Free keywords: circuit reconstruction software; contour detection; algorithm; image segmentation; serial block-face scanning electron microscopy; neural circuits; fly visual system
 Abstract: A new technique, "serial block face scanning electron microscopy" (SBFSEM), allows for automatic sectioning and imaging of biological tissue with a scanning electron microscope. Image stacks generated with this technology have a resolution sufficient to distinguish different cellular compartments, including synaptic structures, which should make it possible to obtain detailed anatomical knowledge of complete neuronal circuits. Such an image stack contains several thousands of images and is recorded with a minimal voxel size of 10-20 nm in the x- and y-direction and 30 mu in Z-direction. Consequently, a tissue block of 1 mm(3) (the approximate volume of the Calliphora vicina brain) will produce several hundred terabytes of data. Therefore, highly automated 3D reconstruction algorithms are needed. As a first step in this direction we have developed semi-automated segmentation algorithms for a precise contour tracing of cell membranes. These algorithms were embedded into an easy-to-operate user interface, which allows direct 3D observation of the extracted objects during the segmentation of image stacks. Compared to purely manual tracing, processing time is greatly accelerated. (c) 2007 Elsevier B.V. All rights reserved.

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Language(s): eng - English
 Dates: 2008-01-30
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 400795
ISI: 000252938400026
DOI: 10.1016/j.jneumeth.2007.07.021
 Degree: -

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Title: Journal of Neuroscience Methods
  Other : J. Neurosci. Meth.
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 167 (2) Sequence Number: - Start / End Page: 349 - 357 Identifier: ISSN: 0165-0270
CoNE: https://pure.mpg.de/cone/journals/resource/954925480594