Department of Electronics, Computer Sciences and Systems, Bologna University, Bologna, Italy.
Comput Methods Programs Biomed. 2010 May;98(2):103-17. doi: 10.1016/j.cmpb.2009.08.008. Epub 2009 Sep 24.
We present a strategy for automatic classification and density estimation of epithelial enveloping layer (EVL) and deep layer (DEL) cells, throughout zebrafish early embryonic stages. Automatic cells classification provides the bases to measure the variability of relevant parameters, such as cells density, in different classes of cells and is finalized to the estimation of effectiveness and selectivity of anticancer drug in vivo. We aim at approaching these measurements through epithelial/deep cells classification, epithelial area and thickness measurement, and density estimation from scattered points. Our procedure is based on Minimal Surfaces, Otsu clustering, Delaunay Triangulation, and Within-R cloud of points density estimation approaches. In this paper, we investigated whether the distance between nuclei and epithelial surface is sufficient to discriminate epithelial cells from deep cells. Comparisons of different density estimators, experimental results, and extensively accuracy measurements are included.
我们提出了一种在斑马鱼早期胚胎发育阶段自动对上皮细胞包绕层（EVL）和深层（DEL）细胞进行分类和密度估计的策略。自动细胞分类为测量不同细胞类别的相关参数（如细胞密度）的变异性提供了基础，其最终目的是估计抗癌药物在体内的有效性和选择性。我们的目标是通过上皮/深层细胞分类、上皮区域和厚度测量以及散点密度估计来实现这些测量。我们的方法基于最小曲面、Otsu 聚类、Delaunay 三角剖分和点云内部密度估计方法。在本文中，我们研究了细胞核与上皮表面之间的距离是否足以区分上皮细胞和深层细胞。比较了不同的密度估计器、实验结果和广泛的精度测量。
