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Abstract
Characterizing dynamic sub-cellular morphologies in response to perturbation remains a challenging and important problem. Many organelles are anisotropic and difficult to segment, and few methods exist for quantifying the shape, size, and quantity of these organelles. The OrNet (Organellar Networks) framework offers a segmentation pipeline to preprocess confocal imaging videos that display various mitochondrial morphologies into social networks. Earlier methods of anomaly detection in organelle structures include manual identification which is time consuming. Thus, we propose the two different methods to perform classification on these organelles that captures their dynamic behaviors and identifies different mitochondrial morphologies. One is a graph deep learning architecture, and the second is an approach that finds a graph representation and uses traditional classifiers. Comparing different classification models will effectively improve the robustness of categorizing distinct structural changes in subcellular organelle structures which is useful for identifying infection patterns, offering a new way to understand cellular health.