Deep Learning Enabled Time-Lapse 3D Cell Analysis

TL;DR

This paper introduces a deep learning-based approach for accurate time-lapse 3D cell analysis, including segmentation, feature extraction, and tracking of individual cells in confocal image stacks, aiding morphogenesis studies.

Contribution

It presents a novel deep feature segmentation, adjacency graph feature extraction, and robust graph tracking methods tailored for 3D cell analysis in time-lapse imaging.

Findings

High accuracy in cell segmentation and labeling

Effective tracking of cells over time

Robustness demonstrated through extensive experiments

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.