Wang Zhenzhou, Li Haixing
State Key Laboratory for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China.
BMC Bioinformatics. 2017 Mar 23;18(1):189. doi: 10.1186/s12859-017-1604-1.
In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work.
To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods.
The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods.
近年来，用于细胞成像的显微镜技术得到了极大且迅速的发展。随之而来的对成像细胞进行自动分割和量化的需求也越来越多。经过在科研和工业应用领域数十年的广泛研究，细胞分割已经取得了巨大进展，尤其是在分割某些特定类型的细胞方面，例如肌肉细胞。然而，它缺乏一个能普遍解决细胞分割问题的框架。相反，人们提出了不同的分割方法来处理不同类型的细胞，这使得研究工作呈现出分散的状态。此外，大多数流行的分割和量化工具通常都需要大量的人工操作。
为了使细胞分割工作更具收敛性，我们在本文中提出了一个能够自动且稳健地分割不同种类细胞的框架。该框架在之前用于分割肌肉细胞的方法基础上进行了改进，通过增加更多的合并情况将其推广到适用于分割和量化各种细胞图像。与之前的方法相比，所提出的框架的分割和量化精度还通过三个新步骤得到了提高：（1）提出并添加了一种简化的校准方法用于阈值选择过程；（2）提出了一种噪声斑点滤波器以去除噪声斑点；（3）提出了一种边界平滑滤波器以减少迭代侵蚀产生的错误种子。事实证明，与之前的方法相比，所提出的框架的量化精度从93.4%提高到了96.8%。此外，在所提出的框架在量化肌肉细胞方面的精度也优于两种现有的最先进方法。
所提出的框架能够比最先进的方法自动分割和量化更多类型的细胞。
