  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.
