A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

分析组织成像数据的一个主要挑战是细胞分割——识别图像中每个细胞的精确边界的任务。为了解决这个问题，我们构建了 TissueNet，这是一个用于训练分割模型的数据集，其中包含超过 100 万个手动标记的细胞，比以前发布的所有分割训练数据集都高出一个数量级。我们使用 TissueNet 来训练 Mesmer，这是一种支持深度学习的分割算法。我们证明了 Mesmer 比以前的方法更准确，可以推广到 TissueNet 中组织类型和成像平台的全部多样性，并达到人类水平的性能。Mesmer 能够自动提取关键细胞特征，例如蛋白质信号的亚细胞定位，这在以前的方法中具有挑战性。然后，我们调整 Mesmer 以利用高度多路复用数据集中的细胞谱系信息，并使用此增强版本来量化人类妊娠期间的细胞形态变化。所有代码、数据和模型都作为社区资源发布。