SpheraSense: A Software Tool to Automate and Standardize the Analysis of Cancer Spheroid Microscopy Images

Abstract

In modern data-intensive biological research, making high-throughput image analysis more automated and standardized can improve key aspects of experimental work – reproducibility of results, time cost and expenses. The initiative is particularly beneficial in the research of spheroids, which simulate in vivo conditions better than the classical cell culturing model. This thesis presents SpheraSense, a software solution which introduces automation to the analysis of spheroid microscopy images, and includes the application of the software on three cancer spheroid experiments involving small cell lung cancer, head and neck cancer, and glioblastoma. SpheraSense successfully captures significant findings in experimental data, matches manual evaluation variability and reduces analysis time considerably. This thesis streamlines the analysis of spheroid experiments, contributing to more efficient and reproducible research practices.