Keywords

Cell Detection, Cell Tracking, Cell Classification

Abstract

Microscopy image processing is critical for precision oncology and immunotherapy, two approaches in cancer treatment often combined to enhance patient outcomes. Numerous scientists have studied the effects of drugs on the immune system and tumors. To quantify the impact of various drugs on different immune and cancer cell types, medical researchers conduct ex vivo assays. In these assays, patient-derived live cells are placed in an artificial microenvironment where drug responses are monitored over time. Brightfield microscopy captures one image every half hour of numerous patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to six days. These images are used to quantify the response of different cells to various drugs. However, tracking thousands of cells in low-resolution, low-frame-rate images remains challenging. Existing digital image processing algorithms require high-resolution images involving very few cells from homogeneous cell line populations. In this work, we propose three novel frameworks to track cancer cells, capture their behavior, and quantify cell viability to inform clinical decisions in a high-throughput manner.

Completion Date

2024

Semester

Summer

Committee Chair

Zhang Wei

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

DP0028594

URL

https://purls.library.ucf.edu/go/DP0028594

Language

English

Rights

In copyright

Release Date

August 2024

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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