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Benchmarking segmentation methods for spatial transcriptomics
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DOI：10.1038/s41592-026-03272-x.png)
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Cell segmentation is a critical yet under-standardized step in spatial transcriptomics, directly affecting cell identity assignment, spatial organization and downstream biological interpretation. Despite rapid advances in spatial transcriptomics technologies, systematic evaluations of segmentation methods across platforms and biological contexts remain limited. Here we benchmark 11 image-based, transcriptomics-based and multimodal segmentation methods across 15 datasets generated by 5 spatial transcriptomics technologies, spanning 12 human and mouse tissue types. We introduce biologically informed evaluation metrics that quantify segmentation quality, transcript recovery, cell composition, morphology, expression purity and spatial organization. Benchmarking reveals substantial variation in performance across datasets, technologies and evaluation criteria, highlighting trade-offs among segmentation completeness, transcriptomic fidelity and biological interpretability. Cross-technology analyses further demonstrate that segmentation choice critically shapes downstream analyses, including cell-type specificity, transcript contamination and ligand–receptor inference. Together, our unified Nextflow pipeline provides a scalable foundation for standardized evaluation of segmentation and method selection in spatial transcriptomics. This Analysis benchmarks segmentation methods across different datasets and presents Nextflow, a pipeline for standardized method selection for spatial transcriptomics.
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