The detection and classification of bacterial colonies in images of agar-plates is important in microbiology, but is hindered by the lack of labeled datasets. Therefore, we propose Colony Grounded SAM2, a zero-shot inference pipeline to detect and segment bacterial colonies in multiple settings without any further training. By utilizing the pre-trained foundation models Grounding DINO and Segment Anything Model 2, fine-tuned to the microbiological domain, we developed a model that is robust to data changes. Results showed a mean Average Precision of 93.1\% and a Dice@detection score of 0.85, showing excellent detection and segmentation capabilities on out-of-distribution datasets. The entire pipeline with model weights are shared open access to aid with annotation- and classification purposes in microbiology.
@article{arxiv.2603.13393,
title = {Colony Grounded SAM2: Zero-shot detection and segmentation of bacterial colonies using foundation models},
author = {Daan Korporaal and Patrick de Kruijf and Ralph H. G. M. Litjens and Bas H. M. van der Velden},
journal= {arXiv preprint arXiv:2603.13393},
year = {2026}
}