Robust Time-Lapse Microscopy Automation: Detection, Tracking, Annotation Transfer, and Reinforcement Learning for Complex Biomedical Systems

Abstract

High-throughput time-lapse phase-contrast microscopy is central to biomedical research and biotechnology because it enables direct observation of living-cell dynamics without genetic modification or phototoxic staining. However, quantitative analysis remains limited by the cost and subjectivity of manual annotation, particularly in dense sequences where cells overlap, boundaries are ambiguous due to optical artifacts, and morphology changes substantially during mitosis. This dissertation addresses these challenges through a unified framework that integrates robust detection, temporally consistent tracking, and unsupervised cross-domain adaptation, thereby establishing foundations for scalable and reproducible automated microscopy analysis.

The research contributes across data infrastructure, algorithmic development, and system-level integration. First, the dissertation establishes a reproducible dataset preparation and annotation methodology for microscopy, including consistent class definitions and a multi-stage labeling workflow that combines expert annotation with model-assisted labeling and temporal refinement. This strategy supports the creation of large, high-fidelity training data while reducing the manual burden associated with dense microscopy scenes.

Second, a comprehensive benchmark compares representative detector families under standardized training and evaluation protocols, emphasizing both accuracy and deployment constraints such as throughput and computational complexity. The resulting analysis provides practical selection guidance for microscopy applications spanning offline analysis and real-time use cases.

Third, the dissertation introduces an integrated detection-tracking pipeline to improve temporal robustness in time-lapse data. The tracking framework strengthens motion modeling through non-linear filtering and strengthens temporal association using enhanced appearance representations, reducing trajectory fragmentation caused by transient detection failures. This enables more reliable longitudinal analysis where complete cell histories are essential.

Fourth, an unsupervised annotation transfer framework is presented to address domain shift across laboratories and acquisition setups. By translating labeled source-domain images into the target-domain appearance while preserving geometric validity of annotations, the approach enables training target-style detectors without manual target labeling. Because target-domain ground truth is unavailable, the dissertation proposes proxy evaluation measures that assess detection reliability through complementary statistical and consensus-based indicators.

Fifth, the dissertation situates microscopy perception within complex AI systems for biomedical and biotechnological automation. It outlines how robust perception outputs (e.g., counts, division-related signals, spatial distributions, and trajectory statistics) can be transformed into state representations for future closed-loop decision-making, and it summarizes reinforcement-learning design considerations relevant to safe and resource-aware optimization in experimental workflows.

Collectively, these contributions reduce key barriers to scalable microscopy automation by enabling efficient dataset construction, principled detector selection, temporally consistent tracking, cross-domain deployment without expensive re-annotation, and a systems-level pathway toward future closed-loop intelligent laboratory platforms.