Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.
BMC Bioinformatics. 2013 Nov 19;14:328. doi: 10.1186/1471-2105-14-328.
Recently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history and cell positions with time. However, current image segmentation algorithms suffer from high error rate especially after 350-cell stage because of low signal-noise ratio as well as low resolution along the Z axis (0.5-1 microns). As a result, correction of the errors becomes a huge burden. These errors are mainly produced in the segmentation of nuclei. Thus development of a more accurate image segmentation algorithm will alleviate the hurdle for automated analysis of cell lineage.
This paper presents a new type of nuclei segmentation method embracing an bi-directional prediction procedure, which can greatly reduce the number of false negative errors, the most common errors in the previous segmentation. In this method, we first use a 2D region growing technique together with the level-set method to generate accurate 2D slices. Then a modified gradient method instead of the existing 3D local maximum method is adopted to detect all the 2D slices located in the nuclei center, each of which corresponds to one nucleus. Finally, the bi-directional prediction method based on the images before and after the current time point is introduced into the system to predict the nuclei in low quality parts of the images. The result of our method shows a notable improvement in the accuracy rate. For each nucleus, its precise location, volume and gene expression value (gray value) is also obtained, all of which will be useful in further downstream analyses.
The result of this research demonstrates the advantages of the bi-directional prediction method in the nuclei segmentation over that of StarryNite/MatLab StarryNite. Several other modifications adopted in our nuclei segmentation system are also discussed.
最近开发了一系列算法，为追踪秀丽隐杆线虫胚胎细胞谱系提供了自动工具。在这些算法中，对共聚焦激光扫描显微镜采集的 3D 图像进行处理，输出结果是具有细胞分裂史的细胞谱系和随时间变化的细胞位置。然而，当前的图像分割算法由于信噪比低以及 Z 轴（0.5-1 微米）分辨率低，在 350 细胞阶段后错误率很高。因此，错误的校正成为一项巨大的负担。这些错误主要发生在细胞核的分割中。因此，开发一种更精确的图像分割算法将减轻自动细胞谱系分析的障碍。
本文提出了一种新型的细胞核分割方法，它包含一个双向预测过程，可以大大减少假阴性错误的数量，这是以前分割中最常见的错误。在这种方法中，我们首先使用二维区域生长技术和水平集方法生成准确的二维切片。然后采用改进的梯度方法代替现有的三维局部最大值方法来检测位于细胞核中心的所有二维切片，每个切片对应一个细胞核。最后，将基于当前时间点前后图像的双向预测方法引入系统，以预测图像质量较低部分的细胞核。我们的方法的结果显示，在准确性方面有了显著的提高。对于每个细胞核，还获得了其精确的位置、体积和基因表达值（灰度值），所有这些都将有助于进一步的下游分析。
该研究的结果表明，双向预测方法在细胞核分割方面优于 StarryNite/MatLab StarryNite。我们的细胞核分割系统中采用的其他几种修改也进行了讨论。
