Bacteria tracking and life cycle state classification using graph neural networks and pretrained vision transformers

For understanding dynamic biological processes such as the life cycle progression of bacteria at the single-cell level, automatic methods for tracking and state classification are needed. In this work, we propose a unified framework based on Graph Neural Networks for simultaneous bacteria tracking, division detection, and life cycle state classification. In previous work, these tasks were treated separately. With our method, trajectories are represented by a graph, where nodes represent bacteria at different time points of a live-cell microscopy video, and edges represent their interactions over multiple frames. Tracking, division detection, and life cycle state classification are performed simultaneously by classifying graph nodes and edges. For all three tasks, we use visual object features from a large-scale pretrained foundation model. This eliminates the need for separately-trained task-specific CNN encoders as used in previous work and enhances the robustness. In addition, we introduce a network-based approach for segmentation error correction using division and multi-frame correspondence predictions. Our method was evaluated using live-cell bright-field microscopy videos of spore germination and outgrowth of rod-shaped bacteria. Our experiments show that the proposed method outperforms existing methods for division detection and state classification. The method yields state-of-the-art results for bacteria tracking and shows increased robustness against segmentation errors as well as image distortions.