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.
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland, Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland.
Bioinformatics. 2014 Sep 15;30(18):2644-51. doi: 10.1093/bioinformatics/btu302. Epub 2014 May 21.
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.
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.
Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip.
通过光学显微镜进行定量单细胞生物学研究，对图像中的细胞进行识别（细胞分割）至关重要。尽管存在大量的分割方法，但准确的分割具有挑战性，通常需要针对算法进行特定问题的调整。此外，目前大多数分割算法都依赖于少数基本方法，这些方法使用图像的梯度场来检测细胞边界。然而，许多显微镜方案可以生成具有细胞膜特征强度分布的图像。这尚未在算法上得到利用，以建立更通用的分割方法。
我们提出了一种自动细胞分割方法，该方法对细胞膜上的信息进行解码，并保证在每个细胞的基础上最佳地检测到细胞边界。图割通过方向互相关来考虑细胞边界的信息，并自动纳入空间约束。该方法可以准确地分割在不同显微镜技术下获取的、具有不同形态的细胞以及在密集培养中生长的细胞的图像。在定量基准测试中以及在对合成和真实图像的与现有方法的比较中，我们证明了即使存在细胞形状不规则、细胞间变异性和图像噪声，分割性能也得到了显著提高。作为概念验证，我们监测了绿色荧光蛋白标记的质膜转运蛋白在单个酵母细胞中的内化。
Matlab 代码和示例可在 http://www.csb.ethz.ch/tools/cellSegmPackage.zip 获得。
