UC San Diego

Morphological study in system biology provides a broader perspective of understanding biological systems’ structure, form, and organization. Nowadays, incorporating state-of-the-art novel vision-AI techniques revolutionizes this study and could accelerate the feature extraction process and lead to groundbreaking discoveries. The design of novel computer vision-based Deep Learning algorithms enables the development of predictive models, which helps in studying disease progression, developing personalized medicines, drug testing, organ replacement, etc. This thesis presents novel procedures and techniques to extract features from confocal and histopathological images to study organoid culture and colorectal cancer. I have successfully created a unique dataset of Crohn’s disease patient-derived organoids (PDOs) and normal colon tissue samples from mice and humans. Organoids need rigorous rapid imaging for continuous monitoring over a long period. Therefore, it is challenging for scientists to process and verify the data manually. Our developed first-of-its-kind novel organoid mining engine process provides a real-time investigation of organoids. The developed model accurately locates, quantifies, tracks, and classifies human colon organoids without expert intervention. Histopathology image analysis is the key to diagnosing colon cancer by focusing on cell morphology and tissue structures. A pathologist takes images from the interest section of the tissue and prepares them for further analysis. The traditional method involves hand-crafted feature extraction followed by classical image processing techniques. I have introduced an original U-shaped crypt segmentation model using novel vision-AI on colon tissue, revealing a new gene expression pattern on the glandular epithelium cells.