美国华盛顿大学Joseph D. Mougous和Paul A. Wiggins共同合作近期取得重要工作进展，他们研究开发了Omnipose，一个独立于形态学的高精度细菌细胞分割解决方案。相关论文于2022年10月17日在线发表于《自然—方法学》杂志上。

研究人员提出了Omnipose，一种深度神经网络图像分割算法。独特的网络输出，如距离场的梯度，使Omnipose能够准确地分割细胞，而目前的算法，包括其前身Cellpose，都会产生错误。研究人员发现Omnipose在混合细菌培养、抗生素处理的细胞和细长或分枝形态的细胞上实现了前所未有的分割性能。

此外，Omnipose的优势可以延伸到非细菌的对象、不同成像模式和三维物体。最后，研究人员展示了Omnipose在描述细菌间拮抗过程中出现的极端形态表型中的效用。Omnipose是一种强大的工具，可以从成像数据中描述不同的以及任意形状的细胞类型。

据介绍，显微镜技术的进步为定量和精确测量细菌单细胞水平上的形态和分子现象提供了很大的保障。然而，这种方法的潜力最终受到独立于细胞形态学或光学特征的可靠细胞分割方法的限制。

附：英文原文

Title: Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Author: Cutler, Kevin J., Stringer, Carsen, Lo, Teresa W., Rappez, Luca, Stroustrup, Nicholas, Brook Peterson, S., Wiggins, Paul A., Mougous, Joseph D.

Issue&Volume: 2022-10-17

Abstract: Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

DOI: 10.1038/s41592-022-01639-4

Source: https://www.nature.com/articles/s41592-022-01639-4