Drug attrition rates for cancer are much higher than in other therapeutic areas. There is an urgent need for appropriate tumor models that can physiologically replicate the key features of human tissue to find optimal strategies to evaluate novel and effective agents and ultimately develop curative chemotherapies. Historically, 2D or traditional monolayer cancer cell lines, with limited representation of the actual tumors they are derived from, have been utilized for cancer and drug development research, as well as expensive and time-consuming patient-derived xenograft models or mutation-based rodent models. However, the realization of the vision of precision and personalized therapy urgently requires the development of individualized tumor models that are low cost, high throughput and broadly applicable to both research and clinical settings. Organoids, multicellular constructs derived from self-organizing stem cells, are three-dimensional ex vivo tissue models that incorporate many of the physiological and genetic features of the in vivo tissue. Organoids promise to provide realistic research models for developmental and stem cell biology such as characterization of fundamental tumor development mechanisms, drug development and screening, and the study of basic biological processes such as cellular nutrient sensing and hormone secretion. Brain, breast, colon, prostate, lung, liver, and pancreas organoids are just some examples of tissues and tumors currently being studied. In this thesis, I will discuss and report a new solution to advance the use of organoids as a widely practical research technique. To achieve this, chemistry, biology, and engineering principles were used to design and develop a highly reproducible, standardized, miniaturized assay that can be performed in both research and clinical settings, utilizing minimal amounts of reagents in a low-cost automated system permitting screening of thousands of conditions. First, I will discuss the optimization, characterization, and validation of an integrated microfluidic based ex vivo tissue and tumor modeling system that is superior to the currently available approaches in terms of ability to facilitate and accelerate both preclinical and foundational research in a high-throughput fashion with optimal user friendliness and reproducibility. Following, I will reveal a new easy to use and open source, computer vision based platform, called OrganoID, that recognizes, labels, and tracks single organoids in brightfield and phase-contrast microscopy images. OrganoID enables straightforward and accurate analysis of organoid images to accelerate the use of organoids as physiologically relevant models in high-throughput research and compatible with both the new microfluidic culture platform as well as more traditional organoid culture methods.