Software and Systems Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
Cytometry A. 2011 Jul;79(7):545-59. doi: 10.1002/cyto.a.21079. Epub 2011 Jun 14.
The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability.
细胞荧光显微镜分析通常需要确定细胞边缘。这通常使用分割技术来完成，该技术将图像中的细胞对象与周围背景分开。本研究比较了应用于两种不同细胞系和五种不同成像条件的九种不同分割技术的分割结果。观察到分割结果存在显著的可变性，这仅仅是由于成像条件或不同算法的应用的差异造成的。我们使用一种新的双变量相似性指数度量标准对结果进行量化和比较，该度量标准评估低估或高估细胞对象的程度。结果表明，常用的基于阈值的分割技术不如具有多个聚类的 k-均值聚类准确。分割准确性随成像条件变化，这些条件决定了细胞边缘的清晰度以及细胞的几何特征。基于这一观察结果，我们提出了一种量化细胞边缘特征的方法，以提供算法执行精度的估计。本研究的结果将有助于制定评估实验室间可比性的标准。
