Data from: Foundation cell segmentation models performance on live microscopy and spatial-omics data

Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the level of downstream biological analysis remain limited. To address this gap, we evaluated several recent segmentation models, including Cellpose cyto3, Cellpose-SAM, µSAM, and CellSAM, on phase contrast and fluorescence cell culture images. In addition, Mesmer and InstanSeg were included for benchmarking on multiplexed fluorescence tissue images generated using CO-Detection by IndEXing (CODEX). We found that Cellpose-SAM achieved strong performance on phase contrast images, while SAM-based models consistently performed well on fluorescence cell culture data. In contrast, no single model consistently outperformed others on CODEX datasets. Instead, each model exhibited distinct strengths and limitations, which led to differences in downstream analyses, including clustering and cell type identification. These results highlight that segmentation performance is highly context-dependent and can influence biological interpretation. Together, our study emphasizes the importance of selecting segmentation models based on dataset characteristics and analytical goals, rather than relying on a single universal approach.

精准的细胞分割是生物成像数据定量分析的关键步骤。近年来深度学习领域的进展推动了通用分割模型的发展，这类模型可在多种成像模态下稳健运行，包括相差成像、荧光细胞培养成像以及多重荧光组织成像。然而，目前针对这些模型在下游生物分析层面的系统性对比研究仍较为有限。为填补这一研究空白，我们针对相差成像与荧光细胞培养图像，评估了多款近年推出的分割模型，包括Cellpose cyto3、Cellpose-SAM、µSAM及CellSAM；此外还纳入了Mesmer与InstanSeg，用于对采用索引共检测（CO-Detection by IndEXing，简称CODEX）技术生成的多重荧光组织图像开展基准测试。研究结果显示，Cellpose-SAM在相差成像图像上表现优异，而基于SAM的各类模型在荧光细胞培养数据上始终保持出色性能；与之形成对比的是，在CODEX数据集上并无某一款模型能始终优于其他模型，实际上各模型均具备独特的优势与局限，这会对下游的聚类、细胞类型鉴定等分析环节产生差异化影响。上述结果表明，细胞分割的性能高度依赖于具体应用场景，且会对生物学解读产生显著影响。综上，本研究强调，应根据数据集特性与分析目标来选择合适的分割模型，而非依赖单一的通用方案。