Department of Electrical and Computer Engineering, University of California, Santa Barbara, USA.
Department of Botany and Plant Pathology, Purdue University, West Lafayette, USA.
Sci Rep. 2023 Mar 1;13(1):3483. doi: 10.1038/s41598-023-29149-z.
This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal.
本文提出了一种延时 3D 细胞分析方法。具体来说，我们考虑了从延时 3D 共聚焦细胞图像堆栈中准确定位和定量分析亚细胞特征以及跟踪单个细胞的问题。细胞的异质性和多维图像的体积对细胞形态发生和发育的全自动分析提出了重大挑战。本文的动机是铺砌细胞生长过程，并构建定量形态发生模型。我们提出了一种基于深度特征的分割方法，以准确检测和标记每个细胞区域。基于邻接图的方法用于提取分割细胞的亚细胞特征。最后，提出了一种使用多个细胞特征的稳健基于图的跟踪算法，用于关联不同时间实例的细胞。我们还在秀丽隐杆线虫荧光核图像上展示了我们跟踪方法的通用性。提供了广泛的实验结果，并证明了所提出方法的鲁棒性。代码可在 GitHub 上获得，该方法可通过 BisQue 门户作为服务提供。
