Simultaneous Segmentation of Cell and Nucleus in Schizosaccharomyces pombe Images with Focus Gradient

Abstract

Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. The first step requires robust segmentation of the cell and the most distinguishable compartments (the nucleus) from images with varying focus conditions and qualities. We developed a segmentation system that can segment transmitted illumination images with focus gradient and varying contrast, and extract cell and nucleus boundaries. Corrections for focus gradient are applied to the image to accurately detect cell membrane and cytoplasm pixels. We use the GVF snake model to segment individual cells, using a novel edge map based on detected cell membrane. Quantitative evaluations show overall good segmentation performance on a large set of images, regardless of different image quality, lighting condition, focus condition and phenotypic profile.