University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Dept. of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
Med Image Anal. 2012 Oct;16(7):1436-44. doi: 10.1016/j.media.2012.05.012. Epub 2012 Jun 21.
In high-throughput applications, accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression and the understanding of cell function. We propose an approach for segmenting cell nuclei which is based on active contours using level sets and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We consider three different well-known energy functionals for active contour-based segmentation and introduce convex formulations of these functionals. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images from different experiments comprising different cell types. We have also performed a quantitative comparison with previous segmentation approaches.
在高通量应用中，准确高效地分割荧光显微镜图像中的细胞对于定量蛋白质表达和理解细胞功能至关重要。我们提出了一种基于活动轮廓的细胞核分割方法，该方法使用水平集和凸能量泛函。与以前的工作相比，我们的方法确定了全局解。因此，该方法不受局部最小值的影响，分割结果也不依赖于初始化。我们考虑了三种不同的基于活动轮廓的分割的著名能量泛函，并引入了这些泛函的凸形式。我们还提出了一种有效计算解的数值方法。我们的方法的性能已经使用来自不同实验的不同细胞类型的荧光显微镜图像进行了评估。我们还与以前的分割方法进行了定量比较。
