Department of Agriculture and Biological Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Sci Rep. 2021 May 10;11(1):9847. doi: 10.1038/s41598-021-88966-2.
Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.
鉴定组织、器官和各种发育系统中的单个细胞是一个研究得很好的问题，因为它是在许多生物学背景下客观分析定量图像的必要组成部分。我们开发了一种基于小波的尺寸相关分割方法，该方法无需任何预处理、滤波或微调步骤即可提供稳健的分割，并且对信噪比具有鲁棒性。与其他常用方法相比，基于小波的方法在真阳性率、精度和分割准确性方面实现了稳健的分割结果。我们将分割程序应用于斑马鱼胚胎发育的全内体核分割、图像配准和核形状分析。这些新的分割方法提供了一种手段，可以在整个三维组织和胚胎中以单细胞精度进行定量模式分析，并且对非均匀和嘈杂的图像数据集具有很高的容忍度。
