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Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits

Overview of attention for article published in Frontiers in Neural Circuits, June 2014
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Title
Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits
Published in
Frontiers in Neural Circuits, June 2014
DOI 10.3389/fncir.2014.00068
Pubmed ID
Authors

Kenneth J. Hayworth, Josh L. Morgan, Richard Schalek, Daniel R. Berger, David G. C. Hildebrand, Jeff W. Lichtman

Abstract

The automated tape-collecting ultramicrotome (ATUM) makes it possible to collect large numbers of ultrathin sections quickly-the equivalent of a petabyte of high resolution images each day. However, even high throughput image acquisition strategies generate images far more slowly (at present ~1 terabyte per day). We therefore developed WaferMapper, a software package that takes a multi-resolution approach to mapping and imaging select regions within a library of ultrathin sections. This automated method selects and directs imaging of corresponding regions within each section of an ultrathin section library (UTSL) that may contain many thousands of sections. Using WaferMapper, it is possible to map thousands of tissue sections at low resolution and target multiple points of interest for high resolution imaging based on anatomical landmarks. The program can also be used to expand previously imaged regions, acquire data under different imaging conditions, or re-image after additional tissue treatments.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 181 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Japan 1 <1%
Brazil 1 <1%
Unknown 177 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 21%
Student > Ph. D. Student 28 15%
Student > Bachelor 16 9%
Professor 15 8%
Student > Master 14 8%
Other 34 19%
Unknown 36 20%
Readers by discipline Count As %
Neuroscience 41 23%
Agricultural and Biological Sciences 40 22%
Engineering 15 8%
Biochemistry, Genetics and Molecular Biology 11 6%
Medicine and Dentistry 8 4%
Other 26 14%
Unknown 40 22%