Automated assessment of growth in neuronal networks

Greenberg, Grant

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https://hdl.handle.net/2142/99992 Copy

Description

Title

Automated assessment of growth in neuronal networks

Author(s)

Greenberg, Grant

Contributor(s)

Popescu, Gabriel

Issue Date

2018-05

Keyword(s)

• Quantitative phase imaging (QPI)
• Neural networks
• Image processing

Abstract

Quantitative Phase Imaging (QPI) is an emerging imaging modality, which has attracted significant interest in the past decade. Built upon the principle of interferometry, QPI measures the difference in optical path-length as the intrinsic contrast mechanism; it images mostly transparent, in vitro objects such as cells and tissue. This label-free capability makes QPI a unique tool for long-term imaging and is increasingly used in neuroscience. It has enabled noninvasive studies of neurons from single cell to network levels. Currently, cell structures and network elements of interest, including dendrites and axons, are manually tracked. This process leads to a bottleneck in analysis, which must be remedied in the near future due to the large amount of data constantly generated. In response to the current challenges, we have adapted image processing methods to create a toolbar which can automatically extract and analyze regions of interest in neural networks. Using these methods, image analysis can be greatly expedited, leading to more effective studies of neural diseases.

Type of Resource

text

Language

en

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http://hdl.handle.net/2142/99992

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