# Joint Multi-frame Detection and Segmentation for Multi-cell Tracking

arXiv: https://arxiv.org/abs/1906.10886 · HTML: https://mlsanity.com/paper/1906.10886/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty](https://mlsanity.com/paper/2403.15011/) (2024)
- [Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies](https://mlsanity.com/paper/2104.13482/) (2021)
- [Deep Learning Enabled Time-Lapse 3D Cell Analysis](https://mlsanity.com/paper/2208.07997/) (2022)
- [Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection](https://mlsanity.com/paper/2403.13412/) (2024)
- [Cell as Point: One-Stage Framework for Efficient Cell Tracking](https://mlsanity.com/paper/2411.14833/) (2024)

## Abstract (arXiv)

Tracking living cells in video sequence is difficult, because of cell morphology and high similarities between cells. Tracking-by-detection methods are widely used in multi-cell tracking. We perform multi-cell tracking based on the cell centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of cells. Detection performance of cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-cell tracking. A mitosis detection algorithm is proposed to detect cell mitosis and the cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, cells can be fine segmented in highly dense cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.