Accurate segmentation of touching cells in multi-channel microscopy images with geodesic distance based clustering
Accurate segmentation of touching cells in multi-channel microscopy images with geodesic distance based clustering
复制标题
基于测地距离的聚类对多通道显微图像中的接触细胞进行精确分割
DOI：
10.1016/j.neucom.2014.01.061
复制
发表时间：
2015-02-03
期刊：
影响因子：
6
通讯作者：
Wong, Stephen T. C.
中科院分区：
文献类型：
--
作者：
Chen, Xu;Zhu, Yanqiao;Wong, Stephen T. C.
多通道显微镜图像通过观察单个细胞的形态学变化，已被广泛用于生物医学研究中的药物和靶点发现。然而，它仍然是具有挑战性的分割密集接触的单个细胞在这样的图像准确和自动。在这里，我们提出了一种基于测地线距离的聚类方法，有效地分割密集接触的细胞在多通道显微镜图像。具体地说，自适应学习计划被引入到迭代调整聚类中心，可以显着提高细胞边界的分割精度。此外，一种新的种子选择过程的基础上核分割建议，以确定图像中的细胞的真实数量。为了验证所提出的方法，我们将其应用于在多通道图像中分割接触的Madin-Darby犬肾（MDCK）上皮细胞，以测量单个细胞内不同的N-Ras蛋白表达模式。实验结果表明，该方法在分割大量接触细胞时具有较好的准确性，并且对多通道显微图像的低信噪比和亮度对比度变化具有较好的鲁棒性。此外，定量比较表明，它优于典型的现有的细胞分割方法。(C)2014爱思唯尔有限公司版权所有。
Multi-channel microscopy images have been widely used for drug and target discovery in biomedical studies by investigating morphological changes of individual cells. However, it is still challenging to segment densely touching individual cells in such images accurately and automatically. Herein, we propose a geodesic distance based clustering approach to efficiently segmenting densely touching cells in multi-channel microscopy images. Specifically, an adaptive learning scheme is introduced to iteratively adjust the clustering centers which can significantly improve the segmentation accuracy of cell boundaries. Moreover, a novel seed selection procedure based on nuclei segmentation is suggested to determine the true number of cells in an image. To validate this proposed method, we applied it to segment the touching Madin-Darby Canine Kidney (MDCK) epithelial cells in multi-channel images for measuring the distinct N-Ras protein expression patterns inside individual cells. The experimental results demonstrated its advantages on accurately segmenting massive touching cells, as well as the robustness to the low signal-to-noise ratio and varying intensity contrasts in multi-channel microscopy images. Moreover, the quantitative comparison showed its superiority over the typical existing cell segmentation methods. (C) 2014 Elsevier B.V. All rights reserved.