Date of Award

2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Complex Systems and Data Science

First Advisor

Chris Danforth

Abstract

Imaging is an immensely powerful tool in biomedical research. Technological advances in the last half century have led to the development of new tools for image analysis, with major strides being made in the last 20 years especially with machine and deep learning. However, researchers still often hit a bottleneck during the image analysis phase of their projects that often leads to delays and sometimes even limits the scope of their studies. In this thesis I demonstrate some of the issues that arise while quantifying images to answer a biological question by using a dataset of fly central nervous system images to elucidate interactions between different cells. I present an overview of the types of methods that can be used to perform this analysis including a discussion of their advantages and disadvantages. Finally, I present steps for creating and validating an automated image analysis pipeline that was used to analyze a large section of the fly ventral nerve cord, akin to the spinal cord. Automating image quantifying allowed us to maximize the size of the dataset analyzed, which revealed subtle patterns in cell-cell interactions that would not have been uncovered with manual quantification of a smaller dataset.

Language

en

Number of Pages

53 p.

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Neurosciences Commons

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