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Front Neuroanat. 2014 Nov 7;8:126. doi: 10.3389/fnana.2014.00126. eCollection 2014.

A workflow for the automatic segmentation of organelles in electron microscopy image stacks.

Author information

1
Center for Research in Biological Systems, National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, CA, USA ; Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
2
Scientific Computing and Imaging Institute, University of Utah Salt Lake City, UT, USA.
3
Center for Research in Biological Systems, National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, CA, USA.
4
Regulatory Biology Laboratory, Salk Institute for Biological Studies La Jolla, CA, USA.
5
Center for Research in Biological Systems, National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, CA, USA ; Department of Bioengineering, University of California San Diego, La Jolla, CA, USA ; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.

Abstract

Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime.

KEYWORDS:

3D electron microscopy; automatic segmentation; electron microscopy; image processing; neuroinformatics; organelle morphology; serial block-face scanning electron microscopy

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