{
  "abstract": "Background Automated microscopy enables large-scale tiled imaging but remains sensitive to variations in alignment, illumination, focus, and sample coverage that may only be detected after acquisition. We present an adaptive pre-acquisition quality-control framework for microscopy ultraviolet surface excitation (MUSE) that uses sparse scout imaging and tile-level metrics to guide acquisition and selective rescanning. Methods The framework quantifies brightness, dynamic range, focus, tissue fraction, and exposure error across the field of view. For example, in a representative 10 x 10 acquisition (100 tiles, 20-megapixel imagery), the mean p99 brightness was 0.525 (CV 10.4%), the mean dynamic range was 0.331, the median focus score was 0.0333, and the median tissue fraction was 0.943. Furthermore, applying a conservative exposure-error threshold (|error| > 0.25) identified 25% of tiles as rescan candidates. Results These results show that compact tile-level metrics can be converted into actionable quality gates, enabling early detection of local acquisition errors and targeted rescanning to improve mosaic consistency while minimizing unnecessary acquisition.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "M. Nunez"
    },
    {
      "name": "B. Davies"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0182kkqy0vkwr.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.22.760904",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760904v1.full",
  "id": "0182kkqy0vkwr",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760904v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "2214|Image Processing Techniques and Applications"
    ]
  ],
  "openalex": "W7223794671",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760904v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0182kkqy0vkwr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:53Z",
      "native_id": "10.64898/2026.10.22.760904",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760904v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Metric-driven Pre-acquisition Diagnostics for Automated Microscopy Ultraviolet Surface Excitation (MUSE)",
  "title_text": "Metric-driven Pre-acquisition Diagnostics for Automated Microscopy Ultraviolet Surface Excitation (MUSE)",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:53Z",
  "url": "https://research.draconitic.com/paper/0182kkqy0vkwr",
  "venue": "bioRxiv",
  "versions": []
}