Molecularly Guided Tissue Segmentation and Recognition in Spatial Transcriptomics

Spatial transcriptomics reveals molecular organization within tissue, but translating these patterns into tissue boundaries and class labels remains challenging. We introduce Molecularly Oriented Spatial Segmentation and Tagging (MOSST), a framework that uses molecular evidence to guide both region proposal and tissue recognition. Spatial gene-expression patterns provide box-and-point prompts for SAM3 on H&E and FICTURE maps, supplemented by H&E proposals where molecular signal is weak. Candidate masks are selected without tissue-region annotations. GPT-6 then classifies each candidate using H&E and a Cell Map displaying annotated cell types within and around its boundary. We evaluate MOSST on ten lung tissue microarrays with 75 confidence-annotated ground-truth regions. Evaluation of all selected candidates yields 37/75 regions both localized and correctly classified under one-to-one matching at intersection-over-union ≥ 0.10. In a separate recognition experiment, candidates are paired to ground-truth regions by geometric overlap and held fixed. Adding Cell Map to H&E increases correct classifications from 33/63 to 42/63 and also outperforms the tested cell-composition text representation. These findings support combining molecularly guided region proposals with candidate-aligned cell-type maps for tissue recognition. Precise boundary delineation and generalization beyond the evaluated cohort require further validation.