Data Mapping Across Compact Cellular Tissues of Varying Morphology

Abstract: A problem with cellular image data from plant tissues is that it is difficult to compile and compare data from different specimens due to their varying morphology. In this thesis, a method for compiling image data from different tissues in a unified geometry is developed and evaluated on early floral buds of Arabidopsis thaliana. The proposed method can transfer subcellular resolution data between tissues on both cell and tissue scale. The images are first aligned on a tissue level using image registration techniques, and it is then possible to determine the cell-to-cell connections between the tissues. After the cell-to-cell connections have been found, the data is decomposed in a form that is easy to transfer between the images. By dividing the cells into sectors formed by angular intervals, it is possible to map data that is asymmetrically distributed across an individual cell's membranes. It is shown that hormone transport simulations based on the raw data and the mapped data of geometries and cell polarity have similar behaviours, indicating that the data distribution remains intact after the mapping. The method shows promise for revisiting older datasets and integrating them with new ones for improved analysis and model optimization.