Motion tracking in phase-contrast microscopic images

Abstract

Analysing cell motility is an important process in medical and biomedical studies since most active cellular functions involve change in shape and movements. Manual observation and analysis of cellular images and data sets is a tedious and error prone task. Therefore designing a reliable automatic cell tracking system could considerably ease the burden on biologists. Because of the limitations of microscopic imaging techniques, together with cell characteristics, analysing biological cells is a challenging task. This thesis proposes novel methods for cell segmentation and tracking as well as mitosis detection. It presents a Graph Cut-based cell segmentation algorithm that is fully automatic and exploits temporal information in video microscopy to achieve better segmentation results. It also presents a cell tracking method based on the network flow algorithm that does not rely on perfect cell segmentation, and uses the information of multiple frames for cell association. The tracking algorithm is able to cope with cells entering or exiting a frame any time. To detect mitosis events, the network flow cell tracking algorithm is extended in a novel way that can detect mitosis events and track their daughter cells afterwards. The proposed methods have been tested on the phase-contrast microscopic videos provided by Garvan Institute of Medical Research. Quantitative and qualitative analysis presented in this thesis show that employing temporal information for both cell segmentation and mitosis detection does improve the results considerably.