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
Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
bioRxiv. 2025 Mar 20:2024.05.03.592249. doi: 10.1101/2024.05.03.592249.
Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized 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, 3D cell segmentation, requiring dense annotation of 2D slices still poses significant 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, >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.
细胞分割是众多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。这得益于图像采集、标注和计算易于扩展。然而，三维细胞分割需要对二维切片进行密集标注，仍然面临重大挑战。手动标记三维细胞以训练广泛适用的分割模型是不可行的。即使在高对比度图像中，标注也模棱两可且耗时。在此，我们开发了一种理论和工具箱u-Segment3D，用于二维到三维分割，它与任何生成基于像素的实例细胞掩码的二维方法兼容。u-Segment3D无需训练数据即可将二维实例分割转换并增强为三维一致实例分割，在11个真实数据集、超过70000个细胞（包括单细胞、细胞聚集体和组织）上得到了验证。此外，u-Segment3D与原生三维分割具有竞争力，在细胞拥挤且形态复杂时甚至表现更优。
