Applications and Challenges of AI-Driven Organoid Image Analysis
DU Xuan1#, YAO Yu2#, LI Yuchen1, CHEN Zaozao1*
As advanced in vitro models capable of recapitulating human tissue architecture and physiological functions, organoids are accelerating the replacement of traditional animal models and emerging as pivotal platforms in developmental biology, precision medicine, and drug screening. Leveraging characteristics such as non-invasiveness and high spatiotemporal resolution, imaging data has become a critical tool for deciphering complex organoid phenotypes. Meanwhile, efficient and precise AI (artificial intelligence) algorithms serve as the technical safeguard enabling the transition from basic research to clinical applications. However, organoid image analysis faces severe challenges, including significant morphological heterogeneity, indistinct boundaries, multi-modal noise interference, and prohibitive annotation costs. This article reviews the current status and developmental trends of AI-driven organoid image analysis. Firstly, it summarizes the mainstream applications and limitations of deep learning-based 2D (two-dimensional) segmentation methods in boundary detection and growth tracking. Subsequently, the review highlights the necessity of transitioning from 2D representations to 3D (three-dimensional) spatiotemporal modeling, positing that 3D segmentation technology is the critical pathway for achieving precise spatial modeling and in-depth functional prediction of organoids. By systematically synthesizing current algorithmic challenges and technological evolutions, this work aims to provide a theoretical reference for elucidating the complex biological mechanisms of organoids and developing AI-aided decision support tools.
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