Automated Dynamic Cellular Analysis in Time-Lapse Microscopy

Shuntaro Aotake¹, Chamidu Atupelage², Zicong Zhang¹, Kota Aoki², Hiroshi Nagahashi², Daisuke Kiga¹

¹Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan.
²Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Yokohama, Japan.

DOI: 10.4236/jbm.2016.43008                

Abstract

Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.

Keywords

High Dimension Feature Analysis, Microscopic Cell Image, Cell Division Cycle Identification, Active Contour Model, K-Means Clustering

Conflicts of Interest

The authors declare no conflicts of interest.

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