{
  "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.\n\nMethods: 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.\n\nResults: 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.\n\nDiscussion: Overall, ICC demonstrates strong potential for colony detection and dataset annotation, providing an effective lightweight framework with iterative user guidance for microbiological imaging tasks.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits and Micro-Nano Electronics, Fudan University",
    "Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Fudan University",
    "Shanghai Municipal Center for Disease Control and Prevention",
    "Lingnan University",
    "Shanghai Turtle Technology Co. Ltd."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jingxing Fan"
    },
    {
      "affs": [
        1
      ],
      "name": "Jinrong Shen"
    },
    {
      "affs": [
        2
      ],
      "name": "Yanqi Zhu"
    },
    {
      "affs": [
        3
      ],
      "name": "Pangen Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "Haofeng Li"
    },
    {
      "affs": [
        4
      ],
      "name": "Gangwei Xu"
    },
    {
      "affs": [
        4
      ],
      "name": "Zhehao Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Dongping Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/904ak6spvys19.bib",
  "cited_by": 0,
  "doi": "10.3389/fmicb.2026.1967912",
  "fulltext_url": null,
  "id": "904ak6spvys19",
  "issns": [
    "1664-302X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fmicb.2026.1967912/full",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7223143862",
  "pdf_url": "https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2026.1967912/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/904ak6spvys19.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:12Z",
      "native_id": "10.3389/fmicb.2026.1967912",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fmicb.2026.1967912/full"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Cell Image Analysis Techniques",
      "subfield": "biophysics"
    }
  ],
  "title": "An interactive class-agnostic detection framework for generalizable colony counting",
  "title_text": "An interactive class-agnostic detection framework for generalizable colony counting",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:12Z",
  "url": "https://research.draconitic.com/paper/904ak6spvys19",
  "venue": "Frontiers in Microbiology",
  "versions": []
}