Organoid Computer Vision: A Survey and Outlook

Abstract

This paper introduces an intersection of computer vision, organoid biology, and artificial intelligence by naming it Organoid Computer Vision (OCV). OCV is emerging as an interdisciplinary field that leverages advanced computational techniques to analyze and interpret organoid imaging data. This survey provides a comprehensive overview of the current state of OCV, exploring its foundational principles, methodologies, and applications. It begins by defining the scope of OCV and highlighting its significance in advancing both organoid research and computer vision techniques. Key contributions from recent studies are analyzed, emphasizing innovations in imaging technologies, data processing algorithms, and machine learning models tailored for organoid analysis. Additionally, we discuss the challenges and limitations faced by researchers in this field, including issues related to data quality, computational complexity, and the integration of biological insights. Looking forward, we outline potential future directions for OCV research, including developing more sophisticated models, applying OCV in personalized medicine, and the ethical considerations of using computer vision in organoid research contexts. This survey aims to provide a valuable resource for researchers and practitioners, fostering further advancements and interdisciplinary collaboration in the field of Organoid Computer Vision.