Automated neurite detection and analysis

Jin, Robert

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https://hdl.handle.net/2142/104020 Copy

Description

Title

Automated neurite detection and analysis

Author(s)

Jin, Robert

Contributor(s)

Popescu, Gabriel

Issue Date

2019-05

Keyword(s)

• Quantitative Phase Imaging (QPI)
• Automated neurites extraction
• Image processing

Abstract

Quantitative phase imaging (QPI) is an emerging imaging modality that 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 that is increasingly used in neuroscience. With the help of QPI, many experiments are implemented, and huge amount of image data are collected. For those experiments which focus on the dry mass change of neurites, an automated detection and analysis method is needed to quickly process the data. CT-FIRE is an existing MATLAB software that can extract neurites in an image automatically and collect the statistic information of those neurites. However, it requires manual parameter inputs and manual data collection. This new method is based on the dataset of the experiment on how the dry mass of neurons changes after traumatic injury in different environments. For different input images, it will dynamically select the input parameter for CT-FIRE. After CT-FIRE completes, it will collect output from CT-FIRE and calculate the average dry mass for the entire dataset. It will filter out false detection from CT-FIRE. In addition, given the location of cell bodies, it will collect all neurites attached to the cell bodies and analyze their average dry mass. With the help of this automated process, all datasets are analyzed without any manual input.

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/104020

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