Automated Cell Segmentation for Micropatterning Microscopy Image

In this paper, we present an automated framework including cell segmentation for quantitative analysis of fluorescence microscopy-based images from protein micropatterning experiments. The goal of such experiments is to identify and quantitate protein-protein interactions in the cell membrane and even in the cytosol of living cells. Therefore, we developed a fast and easy-to-use workflow for μ-patterning analyses. In this publication, we describe a method for the segmentation of patterned cells. It is based on an Unet for the automated segmentation of the patterned cells. Furthermore, an evolutionary strategy allows the estimation of the pattern on the cells. Based on the pattern on the extracted cells and their intensities, additional information about the characteristics of the protein-protein interactions is extracted automatically. As we show in this paper, by additional automated cell segmentation we further automatize the information extraction and provide comprehensive micropatterning analyses.