The increasing use of three-dimensional (3D) large-scale imaging techniques, including nuclear magnetic resonance imaging (NMRI), micro/nano-computed tomography (Micro/Nano-CT), light sheet fluorescence microscopy (LSFM), laser scanning confocal microscopy (LSCM), and volume electron microscopy (Volume EM) has facilitated research at the tissue or cellular level and has led to high-throughput production of images of numerous cellular systems.
The development of optical microscopes, X-ray microscopes, and electron/ion microscopes has provided technical support for biological research from the macroscopic scale to the nanoscale. Unfortunately, most of our present understanding of cellular biology in plants has been obtained by analyzing phenotypes at the population level, which likely masks the differences between cells. Diverse cellular structures confer higher-order functionality to the entire plant system. Plants derive multiple adaptive advantages from their complex multicellular structural organization, which includes intricate molecule interactions, vesicle transport, and cellular interactions. This procedure can be used to determine the character and organization of multicellular plant tissues at high efficiency, including precise parameter identification and polygon-based segmentation of plant cells. Finally, we determined the area, centroid coordinate, perimeter, and Feret’s diameter of the cells and harvested the cell distribution patterns from Voronoï diagrams by setting the threshold at localization density, mean distance, or area. We validated our method using different images captured from Arabidopsis thaliana roots and seeds and Populus tremula stems, including fluorescently labeled images, Micro-CT scans, and dyed sections. This procedure allows for the rapid, accurate, automatic quantification of cell patterns and organization at different scales, from large tissues down to the cellular level.
Here, we developed a high-efficiency procedure to characterize, segment, and quantify plant multicellularity in various raw images using the open-source software packages ImageJ and SR-Tesseler. Therefore, it is essential to develop an efficient method for identifying plant complex multicellularity in raw micrographs in plants. However, complex image processing, including identifying specific cells and quantitating parameters, and high running cost of some image analysis softwares remains challenging. Related searches.The increasing number of novel approaches for large-scale, multi-dimensional imaging of cells has created an unprecedented opportunity to analyze plant morphogenesis.
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