Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan.
Department of Bioengineering, Nagaoka University of Technology, Niigata, Japan.
Dev Growth Differ. 2023 Aug;65(6):311-320. doi: 10.1111/dgd.12871. Epub 2023 Jul 9.
Embryo contour extraction is the initial step in the quantitative analysis of embryo morphology, and it is essential for understanding the developmental process. Recent developments in light-sheet microscopy have enabled the in toto time-lapse imaging of embryos, including zebrafish. However, embryo contour extraction from images generated via light-sheet microscopy is challenging owing to the large amount of data and the variable sizes, shapes, and textures of objects. In this report, we provide a workflow for extracting the contours of zebrafish blastula and gastrula without contour labeling of an embryo. This workflow is based on the edge detection method using a change point detection approach. We assessed the performance of the edge detection method and compared it with widely used edge detection and segmentation methods. The results showed that the edge detection accuracy of the proposed method was superior to those of the Sobel, Laplacian of Gaussian, adaptive threshold, Multi Otsu, and k-means clustering-based methods, and the noise robustness of the proposed method was superior to those of the Multi Otsu and k-means clustering-based methods. The proposed workflow was shown to be useful for automating small-scale contour extractions of zebrafish embryos that cannot be specifically labeled owing to constraints, such as the availability of microscopic channels. This workflow may offer an option for contour extraction when deep learning-based approaches or existing non-deep learning-based methods cannot be applied.
胚胎轮廓提取是胚胎形态定量分析的初始步骤，对于理解发育过程至关重要。光片显微镜的最新发展使得包括斑马鱼在内的胚胎的整体延时成像成为可能。然而，由于数据量庞大以及物体的大小、形状和纹理的可变性，从光片显微镜生成的图像中提取胚胎轮廓具有挑战性。在本报告中，我们提供了一种无需对胚胎进行轮廓标记即可提取斑马鱼囊胚和原肠胚轮廓的工作流程。该工作流程基于使用变化点检测方法的边缘检测方法。我们评估了边缘检测方法的性能，并将其与广泛使用的边缘检测和分割方法进行了比较。结果表明，与 Sobel、高斯拉普拉斯、自适应阈值、多 Otsu 和基于 k-均值聚类的方法相比，所提出方法的边缘检测精度更高，与多 Otsu 和基于 k-均值聚类的方法相比，所提出方法的抗噪性更强。所提出的工作流程对于自动化提取由于微观通道可用性等限制而无法进行特定标记的斑马鱼胚胎的小规模轮廓非常有用。当无法应用基于深度学习的方法或现有的非基于深度学习的方法时，此工作流程可能是提取轮廓的一种选择。
