Colony Grounded SAM2: Zero-shot detection and segmentation of bacterial colonies using foundation models

• URL: http://arxiv.org/abs/2603.13393v1
• Date: Wed, 11 Mar 2026 09:46:17 GMT
• Title: Colony Grounded SAM2: Zero-shot detection and segmentation of bacterial colonies using foundation models
• Abstract summary: We propose a zero-shot inference pipeline to detect and segment bacterial colonies in multiple settings without any further training.<n>By utilizing the pre-trained foundation models Grounding DINO and Segment Anything Model 2, we developed a model that is robust to data changes.<n>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.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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