Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images.

ABSTRACT: 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

SUBMITTER: Jiang J 

PROVIDER: S-EPMC9977871 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature