# Graph Neural Network for Cell Tracking in Microscopy Videos

arXiv: https://arxiv.org/abs/2202.04731 · HTML: https://mlsanity.com/paper/2202.04731/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [Microscopy Cell Segmentation via Convolutional LSTM Networks](https://mlsanity.com/paper/1805.11247/) (2018)
- [Pathfinder Discovery Networks for Neural Message Passing](https://mlsanity.com/paper/2010.12878/) (2020)
- [MPM: Joint Representation of Motion and Position Map for Cell Tracking](https://mlsanity.com/paper/2002.10749/) (2020)
- [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)
- [Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation](https://mlsanity.com/paper/2007.15258/) (2020)
- [Moral Lineage Tracing](https://mlsanity.com/paper/1511.05512/) (2015)
- [Efficient Algorithms for Moral Lineage Tracing](https://mlsanity.com/paper/1702.04111/) (2017)

## Built on by (most cited first, 19 of 19)

- [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)
- [Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps](https://mlsanity.com/paper/2308.04526/) (2023)
- [Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields](https://mlsanity.com/paper/2403.17808/) (2024)
- [Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty](https://mlsanity.com/paper/2403.15011/) (2024)
- [Towards Early Prediction of Human iPSC Reprogramming Success](https://mlsanity.com/paper/2305.14575/) (2023)
- [The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures](https://mlsanity.com/paper/2308.12116/) (2023)
- [Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis](https://mlsanity.com/paper/2404.04903/) (2024)
- [Cell Tracking-by-detection using Elliptical Bounding Boxes](https://mlsanity.com/paper/2310.04895/) (2023)
- [DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking](https://mlsanity.com/paper/2310.19641/) (2023)
- [How To Make Your Cell Tracker Say "I dunno!"](https://mlsanity.com/paper/2503.09244/) (2025)
- [Segment Anything for Cell Tracking](https://mlsanity.com/paper/2509.09943/) (2025)
- [TransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals](https://mlsanity.com/paper/2512.01885/) (2025)
- [Deep Temporal Sequence Classification and Mathematical Modeling for Cell Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms](https://mlsanity.com/paper/2406.19574/) (2024)
- [Predicting the Best of N Visual Trackers](https://mlsanity.com/paper/2407.15707/) (2024)
- [CHOTA: A Higher Order Accuracy Metric for Cell Tracking](https://mlsanity.com/paper/2408.11571/) (2024)
- [Cell as Point: One-Stage Framework for Efficient Cell Tracking](https://mlsanity.com/paper/2411.14833/) (2024)
- [Higher-Order Cell Tracking Transformer](https://mlsanity.com/paper/2607.11754/) (2026)
- [SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting](https://mlsanity.com/paper/2607.29033/) (2026)

## Abstract (arXiv)

We present a novel graph neural network (GNN) approach for cell tracking in high-throughput microscopy videos. By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their associations by its edges, we extract the entire set of cell trajectories by looking for the maximal paths in the graph. This is accomplished by several key contributions incorporated into an end-to-end deep learning framework. We exploit a deep metric learning algorithm to extract cell feature vectors that distinguish between instances of different biological cells and assemble same cell instances. We introduce a new GNN block type which enables a mutual update of node and edge feature vectors, thus facilitating the underlying message passing process. The message passing concept, whose extent is determined by the number of GNN blocks, is of fundamental importance as it enables the `flow' of information between nodes and edges much behind their neighbors in consecutive frames. Finally, we solve an edge classification problem and use the identified active edges to construct the cells' tracks and lineage trees. We demonstrate the strengths of the proposed cell tracking approach by applying it to 2D and 3D datasets of different cell types, imaging setups, and experimental conditions. We show that our framework outperforms current state-of-the-art methods on most of the evaluated datasets. The code is available at our repository: https://github.com/talbenha/cell-tracker-gnn.