TY - Generic T1 - Deep-learning-assisted visualization for live-cell images T2 - IEEE International Conference on Image Processing Y1 - 2017 A1 - Hsueh-Chien Cheng A1 - Cardone, Antonio A1 - Krokos, Eric A1 - Stoica, Bogdan A1 - Faden, Alan A1 - Varshney, Amitabh KW - deep learning KW - live-cell images KW - Visualization AB - 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. JA - IEEE International Conference on Image Processing PB - IEEE CY - Beijing, China ER -