德克萨斯大学Gaudenz Danuser团队宣布他们研究出基于2D分割堆栈的细胞三维分割通用共识方法。该研究于2025年11月11日发表于国际一流学术期刊《自然—方法学》杂志上。

在这里，开发了u-Segment3D理论与工具包，该二维转三维分割方案兼容任何能生成基于像素的细胞实例掩模的二维方法。u-Segment3D在没有训练数据的情况下将2D实例分割转换并增强为3D一致性实例分割，如11个真实数据集所示，包括7万个细胞，跨越单个细胞，细胞聚集体和组织。

此外，u-Segment3D与原生3D分割具有竞争优势，甚至在细胞拥挤和具有复杂形态时优于原生3D分割。

研究人员表示，细胞分割是广泛的基于显微镜的生物学研究的基础。深度学习彻底改变了二维（2D）细胞分割，实现了跨细胞类型和成像模式的通用解决方案。这是由于图像采集、注释和计算的扩展性。然而，三维（3D）细胞分割需要对二维切片进行密集注释，仍然存在很大的挑战。手动标记3D细胞来训练广泛适用的分割模型是令人望而却步的。即使在高对比度的图像中，注释也是含糊不清且耗时的。

附：英文原文

Title: Universal consensus 3D segmentation of cells from 2D segmented stacks

Author: Zhou, Felix Y., Marin, Zach, Yapp, Clarence, Zou, Qiongjing, Nanes, Benjamin A., Daetwyler, Stephan, Jamieson, Andrew R., Islam, Md Torikul, Jenkins, Edward, Gihana, Gabriel M., Lin, Jinlong, Borges, Hazel M., Chang, Bo-Jui, Weems, Andrew, Morrison, Sean J., Sorger, Peter K., Fiolka, Reto, Dean, Kevin M., Danuser, Gaudenz

Issue&Volume: 2025-11-11

Abstract: Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized two-dimensional (2D) cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However, three-dimensional (3D) cell segmentation, requiring dense annotation of 2D slices, still poses substantial challenges. Manual labeling of 3D cells to train broadly applicable segmentation models is prohibitive. Even in high-contrast images annotation is ambiguous and time-consuming. Here we develop a theory and toolbox, u-Segment3D, for 2D-to-3D segmentation, compatible with any 2D method generating pixel-based instance cell masks. u-Segment3D translates and enhances 2D instance segmentations to a 3D consensus instance segmentation without training data, as demonstrated on 11 real-life datasets, comprising >70,000 cells, spanning single cells, cell aggregates and tissue. Moreover, u-Segment3D is competitive with native 3D segmentation, even exceeding when cells are crowded and have complex morphologies.

DOI: 10.1038/s41592-025-02887-w

Source: https://www.nature.com/articles/s41592-025-02887-w