Contrastive learning for cell division detection and tracking in live cell imaging data

Abstract

Fluorescent live-cell microscopy is essential for understanding cellular dynamics by imaging specific molecules, their interactions, and biochemical states in live samples. It is crucial for biological research and drug screening. However, live-cell imaging often requires balancing temporal resolution with cell viability (i.e. conditions enabling cells to thrive) because of photo-toxicity. Consequently, there is increasing interest in lowering temporal resolution to allow for extended observation periods and to study event sequences that uncover causal relationships and mechanistic insights. Tracking cells in video microscopy with low temporal resolution remains a challenge. We introduce a new integrated methodology that uses contrastive learning and graph-based approaches to improve cell division detection and tracking. Our approach employs contrastive learning models to create cell representations that facilitate the detection of cell divisions and enhance cell tracking. Of note, we propose a weakly-supervised constrastive learning approach to build robust temporal cell representations through time-based augmentations. Additionally, we introduce an innovative graph optimization technique to identify cell tracks based on these representations and observed division events. We evaluate our methods on an in-house dataset and public datasets from the Cell Tracking Challenge, achieving substantial performance improvements in both native and reduced temporal resolutions. Our methodology thus enhances adaptability to various temporal resolutions, improving precision and efficiency in live-cell microscopy analysis. This advancement is particularly beneficial for extended drug screening studies, ensuring cell viability and maintaining normal cell homeostasis, which is vital for therapeutic research.