BacLCT: Bacteria Life Cycle Tracking

BacLCT is a unified GNN-based method for simultaneous tracking, division detection, and life cycle state classification of bacteria in time-lapse microscopy. Segmented cells are represented as nodes of a graph and their interactions over time as multi-frame edges. A message-passing GNN classifies the graph edges as correspondence, division, or no correspondence, and the graph nodes as life cycle states. From these predictions, trajectories are reconstructed. The division and multi-frame predictions are used for segmentation error correction, such as for missed detections, early divisions, and incorrect merges. The node features combine learned features from a DINO-pretrained Vision Transformer with handcrafted single-object features, so no task-specific encoder has to be trained.

BacLCT includes pre-trained models for tracking bacteria in bright field and phase contrast images, and for simultaneous tracking and life cycle state classification of B. subtilis spore germination and outgrowth in bright field images. It is also available as a napari plugin.

• Getting started – install and track a sequence.

• User guide – inference, the napari plugin, and training your own models.

• API reference – generated from the docstrings.

Citation

Kunzmann, M., Elizondo-Cantú, M. C., Bischofs, I. B., Rohr, K. Bacteria tracking and life cycle state classification using graph neural networks and pretrained vision transformers. Medical Image Analysis, 104275 (2026). doi:10.1016/j.media.2026.104275

When using the models with DINO-pretrained ViT features, please also cite Caron et al., ICCV 2021.