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Title:Time-lapse study of neural networks using phase imaging with computational specificity (PICS)
Author(s):Kim, Eunjae
Contributor(s):Popescu, Gabriel
Subject(s):Quantitative phase imaging
Gradient light interference microscopy
Time-lapse microscopy
Neuronal growth analysis
Machine learning
Semantic segmentation
Abstract:In life sciences, fluorescent labeling techniques are used to study molecular structures and interactions of cells. However, this type of cell imaging has its own limitations, one of which is that the process of staining the cells could be toxic to the cells and possibly damage them. We are specifically interested in time-lapse imaging of live neurons to study their growth and proliferation. Neurodegenerative diseases are characterized by phenotypic differences in neuron growth and arborization. This thesis proposes a label-free digital staining method using the deep convolutional neural network to address the issues with the previous cell imaging method. Our results show that a deep neural network, when trained on phase images with correct fluorescent labels, can correctly learn the necessary morphological information to successfully predict MAP2 and Tau labels. This inference, in turn, allows us to classify axons from dendrites in live, unlabeled neurons.
Issue Date:2020-05
Date Available in IDEALS:2020-06-11

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