Author

Lun Zhao

Date of Award

August 2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Bo Liang

Abstract

The brain is a complex organ composed of neurons and glial cells. Historically, glial cells were often overlooked in neuroscience research, but their significance has gained increasing recognition in recent years due to mounting evidence demonstrating their pivotal roles in brain function and disease. Notably, a growing body of research highlights the active involvement of astrocytes, the most abundant type of glial cells, in numerous neurological diseases. With the development of microscopy technology, our understanding of astrocytes is becoming more and more abundant. Due to the characteristics of microscopy, tissue scattering and environmental noise, there is a high demand for astrocyte image post processing, including enhancement. Currently machine learning approach can be a popular solution to this problem, but it requires large amount of data to train. To this end, I proposed a high-performance computational framework to help produce simulated image data. My work mainly consists of 1) simulating astrocyte morphology, 2) simulating astrocytic calcium signaling activity, and 3) estimating optical properties under the microscope, and 4) use machine learning methods to de-noise the microscopy images. The proposed framework can also be used to test existing calcium signal extraction algorithms in the future.

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