Deep Learning Enabled Time-Lapse 3D Cell Analysis

• URL: http://arxiv.org/abs/2208.07997v1
• Date: Wed, 17 Aug 2022 00:07:25 GMT
• Title: Deep Learning Enabled Time-Lapse 3D Cell Analysis
• Authors: Jiaxiang Jiang, Amil Khan, S.Shailja, Samuel A. Belteton, Michael Goebel, Daniel B. Szymanski, and B.S. Manjunath
• Abstract summary: This paper presents a method for time-lapse 3D cell analysis. We consider the problem of accurately localizing and quantitatively analyzing sub-cellular features. The code is available on Github and the method is available as a service through the BisQue portal.
• Score: 7.094247258573337
• License: http://creativecommons.org/licenses/by/4.0/
• 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 cell features is proposed for associating cells at different time instances. 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.

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