背景:由于其非破坏性,无标记成像是研究生物过程的重要策略。然而,常规的显微技术,如相衬或DIC遭受阴影投射的伪影,使自动分割具有挑战性。这项研究的目的是比较已发布的分割工作流程步骤的分割功效(图像重建,前景分割,细胞检测(种子点提取)和细胞(实例)分割)来自多个对比显微镜模式的相同细胞的数据集。
结果:我们建立了一系列例程,旨在通过相差获得的培养皿上生长的活贴壁细胞的图像分割,微分干涉对比度,霍夫曼调制对比度和定量相位成像,我们对适用于无标签数据的可用分割方法进行了综合比较.我们证明了执行图像重建步骤至关重要,允许使用最初不适用于无标签图像的分割方法。此外,我们比较了前景分割方法(阈值,特征提取,水平设置,图形切割,以学习为基础),种子点提取方法(高斯的拉普拉斯,径向对称和距离变换,迭代径向表决,最大稳定极值区域和基于学习的)和单细胞分割方法。我们验证了每种显微镜模式的合适方法集,并在线发布。
结论:我们证明了图像重建步骤允许使用原本不打算用于无标记成像的分割方法。除了方法的综合比较,提供了原始和重建的注释数据和Matlab代码。
BACKGROUND: Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities.
RESULTS: We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online.
CONCLUSIONS: We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.