{
  "abstract": "Accurate particle picking remains a critical step in cryo-electron microscopy (cryo-EM) single-particle analysis, and the field has witnessed the development of a broad spectrum of approaches – from classical template-based methods to modern deep learning frameworks – to address this task. Here we present CryoQSeg , a particle segmentation and picking framework that represents a paradigm shift toward leveraging a state-of-the-art visual large model. Built upon Qwen-Image-Edit-2511 as its core backbone, CryoQSeg is fine-tuned through supervised fine-tuning and reinforcement learning on the public CryoPPP benchmark dataset, enabling the generation of high-fidelity, pixel-level particle masks directly from raw micrographs. To translate these masks into precise particle coordinates, we further integrate a hybrid post-processing pipeline that couples Hough circle detection with the segment anything model, forming a seamless end-to-end workflow from raw image input to accurate localization. We evaluate CryoQSeg on five representative benchmark datasets, demonstrating competitive performance across a range of protein types and imaging conditions in comparison to mainstream methods including Topaz and CryoSegNet . Furthermore, we validate the framework on the heterogeneous Crt-SPARTA-gRNA-tDNA complex, where it successfully resolves three distinct oligomeric states – monomer, dimer and tetramer – at high resolutions, despite using a fraction of the micrographs employed in the original study. By bridging modern visual large models with classical and foundation-model-based post-processing strategies, CryoQSeg demonstrates the potential of this emerging paradigm to deliver a robust and automated solution for cryo-EM particle picking, thereby contributing to a solid foundation for high-resolution three-dimensional structural determination of biological macromolecules.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Yunhai Sun",
      "orcid": "0009-0001-3794-8263"
    },
    {
      "name": "Jiahao Zhao",
      "orcid": "0000-0002-8312-7300"
    },
    {
      "name": "Nan Xu",
      "orcid": "0000-0002-9794-7262"
    },
    {
      "name": "Lei Wang"
    },
    {
      "name": "Wei Ding",
      "orcid": "0000-0003-4368-8215"
    },
    {
      "name": "Ming Li",
      "orcid": "0000-0002-5328-5826"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4kdrj72tvh3ma.bib",
  "cited_by": 0,
  "doi": "10.1107/s2052252526009887",
  "fulltext_url": null,
  "id": "4kdrj72tvh3ma",
  "issns": [
    "2052-2525"
  ],
  "landing_url": "https://journals.iucr.org/paper?S2052252526009887",
  "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/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7222115599",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "International Union of Crystallography (IUCr)",
  "ris": "https://research.draconitic.com/paper/4kdrj72tvh3ma.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:54Z",
      "native_id": "10.1107/s2052252526009887",
      "source": "crossref",
      "url": "https://journals.iucr.org/paper?S2052252526009887"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1315|Advanced Electron Microscopy Techniques and Applications",
      "subfield": "structural-biology"
    }
  ],
  "title": "CryoQSeg : robust particle segmentation for cryo-EM with Qwen-Image-Edit",
  "title_text": "CryoQSeg : robust particle segmentation for cryo-EM with Qwen-Image-Edit",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:54Z",
  "url": "https://research.draconitic.com/paper/4kdrj72tvh3ma",
  "venue": "IUCrJ",
  "versions": []
}