UNLABELLED: The open-source platform openBIS (open Biology Information System) offers an Electronic Laboratory Notebook and a Laboratory Information Management System (ELN-LIMS) solution suitable for the academic life science laboratories. openBIS ELN-LIMS allows researchers to efficiently document their work, to describe materials and methods and to collect raw and analyzed data. The system comes with a user-friendly web interface where data can be added, edited, browsed and searched. AVAILABILITY AND IMPLEMENTATION: The openBIS software, a user guide and a demo instance are available at https://openbis-eln-lims.ethz.ch. The demo instance contains some data from our laboratory as an example to demonstrate the possibilities of the ELN-LIMS (Ottoz et al., 2014). For rapid local testing, a VirtualBox image of the ELN-LIMS is also available.

MOTIVATION: Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology via optical microscopy. Although a plethora of segmentation methods exists, accurate segmentation is challenging and usually requires problem-specific tailoring of algorithms. In addition, most current segmentation algorithms rely on a few basic approaches that use the gradient field of the image to detect cell boundaries. However, many microscopy protocols can generate images with characteristic intensity profiles at the cell membrane. This has not yet been algorithmically exploited to establish more general segmentation methods. RESULTS: We present an automatic cell segmentation method that decodes the information across the cell membrane and guarantees optimal detection of the cell boundaries on a per-cell basis. Graph cuts account for the information of the cell boundaries through directional cross-correlations, and they automatically incorporate spatial constraints. The method accurately segments images of various cell types grown in dense cultures that are acquired with different microscopy techniques. In quantitative benchmarks and comparisons with established methods on synthetic and real images, we demonstrate significantly improved segmentation performance despite cell-shape irregularity, cell-to-cell variability and image noise. As a proof of concept, we monitor the internalization of green fluorescent protein-tagged plasma membrane transporters in single yeast cells. AVAILABILITY AND IMPLEMENTATION: Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip.