Files in this item

FilesDescriptionFormat

application/pdf

application/pdfECE499-Sp2018-greenberg.pdf (4MB)
(no description provided)PDF

Description

Title:Automated assessment of growth in neuronal networks
Author(s):Greenberg, Grant
Contributor(s):Popescu, Gabriel
Subject(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.
Issue Date:2018-05
Genre:Other
Type:Text
Language:English
URI:http://hdl.handle.net/2142/99992
Date Available in IDEALS:2018-05-23


This item appears in the following Collection(s)

Item Statistics