School of Mechanical Engineering and Automation, Robotics Institute, Beihang University, Beijing, China.
Université de Bordeaux & CNRS, LOMA, Talence, France.
J Microsc. 2018 May;270(2):188-199. doi: 10.1111/jmi.12673. Epub 2017 Dec 27.
Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively.
自动细胞分割在生物学研究和临床实践中对细胞行为的特征描述起着关键作用。目前，细胞聚类的分割仍然是一个挑战，也是导致错误分割的主要原因。在这项研究中，重点放在负相差图像中聚类细胞的分割上。提出了一种新的方法，通过在初步分割区域内构建灰度加权距离变换（GWDT），结合光强和细胞形状信息。利用构建的 GWDT，可以检测到聚类细胞，然后使用改进的基于区域骨架的方法将其分离。此外，还应用了轮廓扩展操作以优化细胞边界的检测。本文描述了所提出方法的工作原理和详细步骤，并对聚类细胞分割进行了方法评估。结果表明，与其他方法相比，所提出的方法在聚类细胞分割方面取得了更好的性能，聚类细胞和所有细胞的准确率分别达到 85.8%和 97.16%。
