Automatic single-cell segmentation and tracking of bacterial cells in fluorescence microscopy images

Abstract

Automatic single-cell image analysis allows gaining deeper insights into biological processes. We present an approach for single-cell segmentation and tracking of bacterial cells in time-lapse microscopy image data. For cell segmentation we use linear feature detection and a probability map combined with schemes for cell splitting. For cell tracking we propose an approach based on the maximal overlapping area between cells, which is robust regarding cell rotation and accurately detects cell divisions. Our approach was successfully applied to segment and track cells in time-lapse images of the life cycle of Bacillus subtilis. We also quantitatively evaluated the performance of the segmentation and tracking approaches.