# MPM: Joint Representation of Motion and Position Map for Cell Tracking

arXiv: https://arxiv.org/abs/2002.10749 · HTML: https://mlsanity.com/paper/2002.10749/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Graph Neural Network for Cell Tracking in Microscopy Videos](https://mlsanity.com/paper/2202.04731/) (2022)
- [EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths](https://mlsanity.com/paper/2204.10713/) (2022)
- [Trackastra: Transformer-based cell tracking for live-cell microscopy](https://mlsanity.com/paper/2405.15700/) (2024)
- [CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images](https://mlsanity.com/paper/2102.10377/) (2021)
- [Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps](https://mlsanity.com/paper/2308.04526/) (2023)
- [DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy](https://mlsanity.com/paper/2407.17272/) (2024)
- [Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies](https://mlsanity.com/paper/2104.13482/) (2021)
- [DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking](https://mlsanity.com/paper/2310.19641/) (2023)
- [Segment Anything for Cell Tracking](https://mlsanity.com/paper/2509.09943/) (2025)
- [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)
- [Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm](https://mlsanity.com/paper/2504.12676/) (2025)
- [Higher-Order Cell Tracking Transformer](https://mlsanity.com/paper/2607.11754/) (2026)

## Abstract (arXiv)

Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association). Most cell tracking methods perform the association task independently from the detection task. However, there is no guarantee of preserving coherence between these tasks, and lack of coherence may adversely affect tracking performance. In this paper, we propose the Motion and Position Map (MPM) that jointly represents both detection and association for not only migration but also cell division. It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments. We compared the proposed method with current tracking methods under various conditions in real biological images and found that it outperformed the state-of-the-art (+5.2% improvement compared to the second-best).