MCseg: AI agent-guided workflow search for no-code cell segmentation and transcript attribution in spatial transcriptomics
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 iteratively proposed and evaluated combinations of image-processing and segmentation operations against Xenium-derived cell boundaries. In a lung adenocarcinoma development set, fixed-parameter MCseg increased mean panoptic quality from 0.432 to 0.472 relative to an Optuna-tuned two-diameter Cellpose baseline, while a reference-guided calibration analysis reached 0.554. In an independent expert-annotated colorectal cancer region, MCseg showed higher lineage recall and micro-F1 than the StarDist-based ENACT workflow among cells covered by both methods. Across 15 colorectal cancer regions, MCseg increased neighborhood expression discordance and reduced lineage-exclusive co-expression relative to Space Ranger at similar UMI density. The fixed workflow also transferred to fresh-frozen breast cancer without tissue-specific architecture search, illustrating an agent-guided route to reproducible, locally deployable cell-level spatial transcriptomic analysis.