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

Macke, JH; Maack, N; Gupta, R; Denk, W; Scholkopf, B; Borst, A; (2008) Contour-propagation algorithms for semi-automated reconstruction of neural processes. J NEUROSCI METH , 167 (2) 349 - 357. 10.1016/j.jneumeth.2007.07.021.

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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.

Type: Article
Title: Contour-propagation algorithms for semi-automated reconstruction of neural processes
DOI: 10.1016/j.jneumeth.2007.07.021
Keywords: circuit reconstruction software, contour detection, algorithm, image segmentation, serial block-face scanning electron microscopy, neural circuits, fly visual system, CONFOCAL IMAGE STACKS, SEGMENTATION, NEURONS
UCL classification: UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
URI: http://discovery.ucl.ac.uk/id/eprint/168322
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