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 hyperparameters
in different experimental settings. Here, we present a multi-modality cell
segmentation benchmark, comprising over 1500 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针对特定模态或需要手动干预以指定不同实验设置中的超参数。本研究提出了一个多模态细胞分割基准，包括超过 1500 个带标签图像，来自 50 多个不同的生物实验。前几名的参与者开发了一种基于 Transformer 的深度学习算法，不仅超越了现有方法，还可以应用于不同显微成像平台和组织类型的各种显微图像，无需手动参数调整。这个基准和改进的算法为显微成像中更准确和多功能的细胞分析提供了有希望的途径。