# Deep Learning Enabled Time-Lapse 3D Cell Analysis

arXiv: https://arxiv.org/abs/2208.07997 · HTML: https://mlsanity.com/paper/2208.07997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Cell Tracking via Proposal Generation and Selection](https://mlsanity.com/paper/1705.03386/) (2017)
- [Joint Multi-frame Detection and Segmentation for Multi-cell Tracking](https://mlsanity.com/paper/1906.10886/) (2019)
- [Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection](https://mlsanity.com/paper/1710.06608/) (2017)
- [Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement](https://mlsanity.com/paper/1902.04729/) (2019)
- [Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy](https://mlsanity.com/paper/2010.13343/) (2020)

## Abstract (arXiv)

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.