Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

细胞分割是显微图像中定量单细胞分析的关键步骤。现有的细胞分割方法通常是针对特定模式量身定制的，或者需要手动干预来指定不同实验设置中的超参数。在这里，我们提出了一个多模态细胞分割基准，包括来自 50 多个不同生物实验的 1,500 多个标记图像。顶级参与者开发了一种基于 Transformer 的深度学习算法，该算法不仅超越了现有方法，而且还可以应用于跨成像平台和组织类型的各种显微图像，而无需手动调整参数。该基准和改进的算法为显微成像中更准确和更通用的细胞分析提供了有希望的途径。