Quantitative Image Analysis and Modelling of Phallusia Oocytes and Caenorhabditis elegans Egg

Abstract

Circle packing problem refers to most number of circles which could be fit inside a boundary. Asters or radial microtubules are closely associated with the geometry of a cell. Spatial patterns of microtubule motors are responsible for multiple cellular structures like mitotic spindles. Computational modelling and experimental image analysis combined have shown that these systems can self-organize and form structures even in minimal systems consisting of asters and motors . It has been seen that that microtubules combined with molecular motors can self-organize to form patterns in space that match the rules of the circle packing problem. The sensitivity of the self-organized patterns that emerge from it depends on multiple factors such as concentration, kinetic rates and geometry. The results were obtained in 2D (experimental and simulation) despite having a 3D Phallusia oocyte as a sample organism. This report attempts to expand their work from 2D to 3D, by creating a new tessellation and simulation model. Caenorhabditis elegans is a transparent nematode which is a popular model organism for studying embryonic development. Intraspecific variation in egg shell size and shape are the result of mutations. This thesis deals with the creation of a eggshell detection pipeline based on convolution neural network, U-net. A semi-automatic segmentation algorithm creates ground truth data, which is then used to train U-net, a convolution neural network designed for image segmentation. The pipeline will be used to study the changes in morphometry of eggshell in C. elegans and among related species, leading to how the morphometry changed across evolutionary time scales. The segmentation pipeline could also be used to segment Phallusia oocytes in the future, owing the the samples approximately round shapes