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

Macke, Jakob H.; Maack, Nina; Gupta, Rocky; Denk, Winfried; Schölkopf, Bernhard; Borst, Alexander

doi:10.1016/j.jneumeth.2007.07.021

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

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Denk, Winfried
Department of Biomedical Optics, Max Planck Institute for Medical Research, Max Planck Society;

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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002C-078C-9

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 nm 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.