# CellSPA

Cell segmentation in spatial transcriptomics has lacked a cross-platform comparison with biologically meaningful metrics. A benchmark evaluates 11 image-based, transcriptomics-based and multimodal methods across 15 datasets from 5 technologies and about 2.3 million cells, scoring segmentation quality, transcript recovery, morphology, expression purity and spatial organization. On the average of 15 metrics, SAM scores highest (0.58), ahead of SAM2 (0.53), BIDCell (0.52) and SCS (0.48); after adjusting by the ratio of segmented to reference cells, SCS leads (0.34), with BIDCell and Baysor both at 0.33.

Cell segmentation in spatial transcriptomics has lacked a cross-platform comparison with biologically meaningful metrics. A benchmark evaluates 11 image-based, transcriptomics-based and multimodal methods across 15 datasets from 5 technologies and about 2.3 million cells, scoring segmentation quality, transcript recovery, morphology, expression purity and spatial organization. On the average of 15 metrics, SAM scores highest (0.58), ahead of SAM2 (0.53), BIDCell (0.52) and SCS (0.48); after adjusting by the ratio of segmented to reference cells, SCS leads (0.34), with BIDCell and Baysor both at 0.33.