In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Will defend her dissertation
Dendritic spines form the postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by biologists. Determining the relationship between the dendritic spine morphology change and its functional properties such as memory learning is a fundamental while challenging problem in neuron science research. High throughput neuron image processing is an important method for drug screening and quantitative neurobiological studies. The automated fluorescence microscopy imaging techniques make it possible and facile to visualize the complicated biological processes on the cellular and molecular levels and allow fast and cheap acquisition of such imaging data. With this method, a huge number of images are generated. Therefore, how to efficiently and accurately extract and track the morphology features of spines in time-lapse microscopy neuron images is crucial to answer aforementioned neurobiology questions. In this research, two novel automatic approaches for dendritic spine detection and tracking in time-lapse microscopy neuron images are proposed. Our dendritic spine detection approach is a surface-based approach. Comparing with traditional image analysis based approaches, the advantages of surface-based approaches are: (1) Physical smoothness of the neuron shapes is guaranteed, and geometric measurements can be easily estimated. (2) Instead of analyzing every voxel inside the object, surface-based approach only analyzes vertices on the surface. This representation will significantly reduce the computational cost. Our dendritic spine tracking algorithm can track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on non-rigid registration and integer programming. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images.