{
  "id": 9800044,
  "title": "MCseg: AI agent-guided workflow search for no-code cell segmentation and transcript attribution in spatial transcriptomics",
  "url": "https://urgent.news/2026/09/25/mcseg-ai-agent-guided-workflow-search-for-no-code-cell-segmentation",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-25T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.20.752837v1?rss=1"
  },
  "original_language": "en",
  "account": "Cell-level analysis in high-resolution spatial transcriptomics requires precise segmentation and transcript assignment. Current workflows often prioritize boundary purity over transcript capture and demand significant image-analysis expertise. To address these challenges, a downloadable no-code platform called MCseg was developed. This platform utilizes a fixed segmentation engine generated through an AI-agent-guided search process. During this search, an AI agent iteratively suggested and assessed various combinations of image-processing and segmentation operations, with the Xenium-derived cell boundaries serving as the evaluation standard.\n\nIn a lung adenocarcinoma development set, MCseg improved the mean panoptic quality from 0.432 to 0.472 compared to an Optuna-tuned two-diameter Cellpose baseline. Additionally, a reference-guided calibration analysis achieved a score of 0.554. In an independent expert-annotated colorectal cancer region, MCseg outperformed the StarDist-based ENACT workflow in terms of lineage recall and micro-F1 among cells covered by both methods.\n\nWhen compared to Space Ranger at similar UMI density across 15 colorectal cancer regions, MCseg increased neighborhood expression discordance and reduced lineage-exclusive co-expression. Importantly, the fixed workflow also transferred to fresh-frozen breast cancer samples without the need for tissue-specific architecture search. This demonstrates the potential of an agent-guided approach for achieving reproducible, locally deployable cell-level spatial transcriptomic analysis.",
  "summary": "Cell-level analysis of high-resolution spatial transcriptomics depends on accurate segmentation and transcript assignment, yet current workflows often trade transcript capture for boundary purity and can require substantial image-analysis expertise. We developed MCseg, a downloadable no-code platform whose fixed segmentation engine was derived by an AI-agent-guided search in which an AI agent…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}