Date of Award

2-2013

Embargo Period

4-12-2013

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Robotics Institute

Advisor(s)

Takeo Kanade, Stephen E. Fienberg

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 a certain type of differentiation, namely muscle myotubes, have never been detected without fluorescent labeling. We address these challenging problems by developing computer vision algorithms adopting phase contrast microscopy. We also significantly improve the accuracy of mitosis detection and cell tracking in phase contrast microscopy 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.

Comments

CMU-RI-TR-13-06

Included in

Robotics Commons

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