%0 Conference Proceedings %B IEEE International Conference on Image Processing %D 2017 %T Deep-learning-assisted visualization for live-cell images %A Hsueh-Chien Cheng %A Cardone, Antonio %A Krokos, Eric %A Stoica, Bogdan %A Faden, Alan %A Varshney, Amitabh %K deep learning %K live-cell images %K Visualization %X Analyzing live-cell images is particularly challenging because cells move at the same time they undergo systematic changes. Visually inspecting live-cell images therefore involves simultaneously tracking individual cells and detecting relevant spatio-temporal changes. The high cognitive burden of such a complex task makes this kind of analysis inefficient and error-prone. In this paper we describe a deep-learning-assisted visualization based on automatically derived high-level features to identify target cell changes in live-cell images. Applying a novel user-mediated color assignment scheme that maps abstract features into corresponding colors, we create color-based visual annotations that facilitate visual reasoning and analysis of complex time varying live-cell imagery datasets. The visual representations can be used to study temporal changes in cells, such as the morphological changes in cell at various stages of life cycle. %B IEEE International Conference on Image Processing %I IEEE %C Beijing, China %8 09/2017 %G eng