Panconi Luca, Tansell Amy, Collins Alexander J, Makarova Maria, Owen Dylan M
Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK.
College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK.
Biol Imaging. 2023 Dec 14;4:e1. doi: 10.1017/S2633903X23000260. eCollection 2024.
Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication.
图像分析技术为解释显微镜数据提供了客观且可重复的统计信息。在更高维度上，三维（3D）体积和时空数据突出了静态二维焦平面之外的其他属性和行为。然而，维度增加带来了更高的复杂性，并且现有的3D数据通用分割技术要么很原始，要么高度专门针对特定的生物结构。借鉴二维拓扑数据分析（TDA）的原理，我们制定了一种3D分割算法，该算法实现了持久同调以识别图像强度的变化。由此，我们分别推导出适用于空间和时空数据的两个独立变体。我们证明，这种分析在模拟数据上产生了灵敏且特异的结果，并且可以区分荧光显微镜图像中的突出生物结构，无论其形状如何。此外，我们强调了时间TDA在追踪细胞谱系以及细胞和细胞器复制频率方面的功效。
