Organoid Image

Organoid image analysis focuses on developing computational methods to automatically quantify and interpret images of three-dimensional cell cultures, called organoids, which mimic the structure and function of organs. Current research emphasizes automated segmentation and classification of organoids using deep learning architectures like U-Nets and ResNets, often incorporating self-supervised learning techniques to overcome the limitations of limited labeled data and employing algorithms like correlation clustering to group similar organoids. These advancements are crucial for accelerating drug discovery, personalized medicine, and fundamental biological research by enabling high-throughput analysis of organoid morphology and response to stimuli, replacing time-consuming manual analysis.