Cell Resolution 3D Reconstruction of Developing Multilayer Tissues from Sparsely Sampled Volumetric Microscopy Images

Understanding of the growth dynamics in developmental biology is often pursued through the analysis of cell sizes and shapes obtained from CLSM based imaging at cell resolution of multi-layer tissues. This necessitates the development of robust 3D reconstruction methods using such images. However, all of the current methods of 3D reconstruction using CLSM imaging require large number of cell slices. But in the case of live cell imaging, i.e., imaging a growing tissue, such high depth resolution is not feasible in order to avoid photodynamic damage to the growing cells from prolonged exposure to laser radiation. In this work, we have addressed the problem of 3D reconstruction at cell resolution of a developing multi-layer tissue in the plant meristem when the amount of data is as limited as two to four slices per cell. This introduces significant image analysis challenges in terms of sparsity of the data, low signal-to-noise ratio, and a wide range of shapes and sizes. Motivated by the physical structure of the cells, we propose to reconstruct a cell cluster as a packing of truncated ellipsoids representing the individual cells. We test the proposed computational method on time-lapse CLSM images of Shoot Apical Meristem (SAM) cells of model plant Arabidopsis Thaliana. We show that the 3D reconstruction can lead to 3D shape models of complete cell clusters, which is an essential first step towards obtaining growth statistics for individual cells.