Nejati Javaremi Alireza, Unsworth Charles P, Graham E Scott
Department of Engineering Science, University of Auckland, Auckland, New Zealand.
Centre for Brain Research, University of Auckland, Auckland, New Zealand.
PLoS One. 2013 Dec 17;8(12):e82883. doi: 10.1371/journal.pone.0082883. eCollection 2013.
The problem of automated segmenting and tracking of the outlines of cells in microscope images is the subject of active research. While great progress has been made on recognizing cells that are of high contrast and of predictable shape, many situations arise in practice where these properties do not exist and thus many interesting potential studies - such as the migration patterns of astrocytes to scratch wounds - have been relegated to being largely qualitative in nature. Here we analyse a select number of recent developments in this area, and offer an algorithm based on parametric active contours and formulated by taking into account cell movement dynamics. This Cell-Derived Active Contour (CDAC) method is compared with two state-of-the-art segmentation methods for phase-contrast microscopy. Specifically, we tackle a very difficult segmentation problem: human astrocytes that are very large, thin, and irregularly-shaped. We demonstrate quantitatively better results for CDAC as compared to similar segmentation methods, and we also demonstrate the reliable segmentation of qualitatively different data sets that were not possible using existing methods. We believe this new method will enable new and improved automatic cell migration and movement studies to be made.
显微镜图像中细胞轮廓的自动分割与跟踪问题是当前活跃的研究课题。尽管在识别高对比度和形状可预测的细胞方面已经取得了很大进展，但在实际应用中，仍存在许多不具备这些特性的情况，因此许多有趣的潜在研究——比如星形胶质细胞向划痕伤口的迁移模式——在很大程度上仍停留在定性研究阶段。在此，我们分析了该领域近期的一些进展，并提出了一种基于参数活动轮廓且考虑了细胞运动动力学的算法。我们将这种细胞衍生活动轮廓（CDAC）方法与相衬显微镜的两种最先进分割方法进行了比较。具体而言，我们处理了一个非常困难的分割问题：非常大、薄且形状不规则的人类星形胶质细胞。与类似的分割方法相比，我们定量地证明了CDAC方法具有更好的结果，并且还展示了使用现有方法无法实现的对定性不同数据集的可靠分割。我们相信这种新方法将使新的和改进的自动细胞迁移与运动研究成为可能。
