UC Santa Barbara

Time lapse 3D images are important resources for biology research because they notonly provide 3D structural information but also provide temporal information. Much of the analysis of these data are manual and subjective, and does not scale well with the large amount of imaging data that is routinely collected these days. This is especially true for 3D and time-lapse imagery. Fundamental problems such as detecting cells, subcellular features, tracking of such structures in 3D over time, and modeling 3D shapes in robust manner, remain. The problems addressed in this dissertation are motivated by two bio-imaging problems:

1. Understanding the plant pavement cell growth pattern. The pavement cell growthcontrols the leaf expansion patterns and rates which are the key determinants of the overall photosynthetic rates of the canopy. Time lapse image stacks from 3D confocal imagery are a good resource to study the pavement cell growth process. To better understand this process, machine learning methods are developed to detect and track cellular and sub-cellular features. We developed a deep learning enabled time-lapse 3D analysis pipeline that includes novel boundary tagged 3D segmentation method, and a graph based sub-cellular feature extraction and tracking method. Detailed quantitative evaluation results demonstrate the robustness and state-of-the art performance of the proposed methods.

2. Neuron morphology analysis. Cell morphology especially neuron morphology playsan important role in biology because neuron functions are closely related to neuron morphology. In this dissertation, we propose a robust computational 3D skeleton model to analyze neuron morphology. It is the first deep learning method to compute a 3D neuron skeleton model directly from discrete 3D surface points for neuron classification. The main innovation is in formulating the learning problem associated with computing the medial axis transform that represents the 3D skeleton. We apply our method on two Datasets, Ciona neuron dataset and C.elegan neuron dataset for classification. It results in an accurate and robust skeleton representation, and achieves state-of-the-art performance in classifying neuron types.

The implementation of the methods developed above and the associated data aremade available on GitHub and also as a software service through the UCSB BisQue platform.