Event Location: GHC 4405

Abstract: Time-lapse live cell imaging has been increasingly employed by biological and biomedical
researchers to understand the underlying mechanisms in cell physiology and development by
investigating behavior of cells. This trend has led to a huge amount of image data, the analysis of
which becomes a bottleneck in related research. Consequently, how to efficiently analyze the
data is emerging as one of the major challenges in the fields. Computer vision analysis of non-
fluorescent microscopy images, representatively phase-contrast microscopy images, promises to
realize a long-term monitoring of live cell behavior with minimal perturbation and human
intervention. To take a step forward to such a system, this thesis proposes computer vision
algorithms that monitor cell growth, migration, and differentiation by detecting three cellular
events—mitosis (cell division), apoptosis (programmed cell death), and differentiation—and
tracking individual cells. Among the cellular events, to the best our knowledge, apoptosis and
differentiation have never been detected without fluorescent labeling. We also significantly
improve the accuracy of mitosis detection and cell tracking over previous methods, particularly
under non-trivial conditions, such as high cell density or confluence. We demonstrate the
usefulness of our methods in biological research by analyzing cell behavior in scratch wound
healing assays. The automated system that we are pursuing would lead to a new paradigm of
biological research by enabling quantitative and individualized assessment in behavior of a large
population of intact cells.

Committee:Takeo Kanade, Co-chair

Stephen E. Fieberg, Co-chair

Robert F. Murphy

Fernando De la Torre

Alan J. Russell, University of Pittsburgh