Accurate cell segmentation in microscopy images using membrane patterns.
BACKGROUND
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.


METHODS
Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip.
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland.
Sotiris
S
Dimopoulos
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland.
Christian E
CE
Mayer
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland.
Fabian
F
Rudolf
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland.
Joerg
J
Stelling
D000465:Algorithms; D002462:Cell Membrane / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*