Colony Grounded SAM2: Zero-shot detection and segmentation of bacterial colonies using foundation models
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 e...
| Authors | Daan Korporaal, Patrick de Kruijf, Ralph H. G. M. Litjens et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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