Year: 2026
Type: article
Abstract: Introduction Microbiological monitoring is an essential component of laboratory and industrial quality-control workflows, yet accurate colony counting remains challenging to generalize across diverse colony types and imaging conditions. To address this challenge, we propose the Interactive Colony Counter (ICC), a lightweight and class-agnostic detection framework for microbial colony counting. Methods Aiming to improve generalization across diverse conditions, ICC takes the entire colony plate image as input and iteratively integrates user guidance, in the form of bounding boxes for target colonies and corrections of detection errors. ICC consists of three components: the Cross Stage Partial Fusion Network (CSPFNet) as the backbone, the Contrastive-Guidance Encoder (CGE) for processing user guidance, and the Multi-scale Detection Module (MDM) as the detection head. By training on the AGAR dataset with a class-agnostic strategy, ICC learns general colony features rather than relying on type-specific labels. Results Under simulated user guidance, experimental results show that ICC achieves competitive performance on the AGAR dataset, with a mean Average Precision (mAP) at IoU 0.5:0.95 of 60.7 and 62.9% at different resolutions. To assess generalization beyond the training domain, ICC is further evaluated on the ADBC dataset under different imaging conditions, which also includes species not represented in the training data. In addition, we demonstrate the feasibility of ICC-assisted annotation by constructing the B. lactis -Colony dataset for colony detection, which is generated from Bifidobacterium lactis and constructed using the ICC-assisted annotation workflow. Discussion Overall, ICC demonstrates strong potential for colony detection and dataset annotation, providing an effective lightweight framework with iterative user guidance for microbiological imaging tasks.
Source: Frontiers in Microbiology
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223143862