Abstract
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.
Original language | English |
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Article number | e1 |
Journal | Biological Imaging |
Volume | 4 |
Early online date | 14 Dec 2023 |
DOIs | |
Publication status | Published - 1 Jan 2024 |
Bibliographical note
Funding statementWe acknowledge funding from Oxford Nanoimaging (ONI) and the Engineering and Physical Sciences Research Council through the University of Birmingham CDT in Topological Design, grant code EP/S02297X/1.
Keywords
- fluorescence microscopy
- cell segmentation
- cell tracking
- R
- topological data analysis