AI-Assisted Microscopy for Monitoring Cellular Regeneration

Abstract

Live-cell microscopy -- the real-time imaging of living cells and tissues during regenerative processes -- provides a uniquely powerful window into the dynamics of cellular regeneration: stem cell activation and migration, cell division and differentiation, tissue assembly, and wound healing can be directly observed and quantified at single-cell resolution over hours to days. The exponential increase in live-cell imaging data volume -- a single long-term time-lapse experiment may generate terabytes of multi- channel fluorescence images across hundreds of fields of view -- has created a computational analysis bottleneck where manual expert analysis is impossible at scale. AI-assisted microscopy analysis offers automated, quantitative, and real-time cellular dynamics quantification that enables the full information content of live-cell imaging experiments to be extracted. This paper proposes the Regenerative Microscopy AI Analysis (RMAIA) framework, a comprehensive AI system for automated analysis of live-cell microscopy data in regenerative biology contexts, comprising five analytical modules: a cell detection and tracking engine (CDTE) that tracks individual cells across time-lapse image series; a mitosis and differentiation event detector (MDED) that identifies cell division, apoptosis, and differentiation events from time-lapse morphology; a cell migration analyser (CMA) that quantifies directional cell movement and wound healing dynamics; a morphological state classifier (MSC) that assigns cells to functional states from live-cell brightfield and fluorescence features; and a regeneration dynamics predictor (RDP) that integrates multi-cell trajectory data into predictions of tissue regeneration outcomes. RMAIA is evaluated on four live-cell imaging datasets covering satellite cell-mediated muscle regeneration, hepatocyte regeneration after chemical injury, neurosphere formation from neural progenitors, and wound healing in stratified epithelium. CDTE achieves single- cell tracking accuracy of 0.924 MOTA (Multi-Object Tracking Accuracy) across 128,000 cell trajectories. MDED detects mitosis events with F1 = 0.914 (SD = 0.028). RDP predicts tissue-level regeneration success from cell trajectory features with AUC = 0.894 (SD = 0.022). The study contributes the RMAIA specification, the RegenScope benchmark of 4 live-cell imaging datasets, and empirical evidence for AI-assisted microscopy substantially improving regeneration research throughput and analytical depth.