# EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths

arXiv: https://arxiv.org/abs/2204.10713 · HTML: https://mlsanity.com/paper/2204.10713/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Learning in Video Multi-Object Tracking: A Survey](https://mlsanity.com/paper/1907.12740/) (2019)
- [Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth](https://mlsanity.com/paper/1906.11109/) (2019)
- [Spatial-Temporal Relation Networks for Multi-Object Tracking](https://mlsanity.com/paper/1904.11489/) (2019)
- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [Graph Neural Network for Cell Tracking in Microscopy Videos](https://mlsanity.com/paper/2202.04731/) (2022)
- [Microscopy Cell Segmentation via Convolutional LSTM Networks](https://mlsanity.com/paper/1805.11247/) (2018)
- [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)
- [Cell Tracking via Proposal Generation and Selection](https://mlsanity.com/paper/1705.03386/) (2017)
- [VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning](https://mlsanity.com/paper/2106.11480/) (2021)
- [SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter](https://mlsanity.com/paper/1508.06464/) (2015)
- [Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines](https://mlsanity.com/paper/1608.01276/) (2016)

## Built on by (most cited first, 16 of 16)

- [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)
- [Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics](https://mlsanity.com/paper/2411.00552/) (2024)
- [Generative modeling of living cells with SO(3)-equivariant implicit neural representations](https://mlsanity.com/paper/2304.08960/) (2023)
- [SynCellFactory: Generative Data Augmentation for Cell Tracking](https://mlsanity.com/paper/2404.16421/) (2024)
- [Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images](https://mlsanity.com/paper/2310.08501/) (2023)
- [Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty](https://mlsanity.com/paper/2403.15011/) (2024)
- [Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy](https://mlsanity.com/paper/2210.11441/) (2022)
- [The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures](https://mlsanity.com/paper/2308.12116/) (2023)
- [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)
- [Segment Anything for Cell Tracking](https://mlsanity.com/paper/2509.09943/) (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)
- [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)
- [Self-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials](https://mlsanity.com/paper/2510.07853/) (2025)

## Abstract (arXiv)

A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell segmentation, there are few approaches for simultaneous cell segmentation and tracking using deep learning. Here, we present EmbedTrack, a single convolutional neural network for simultaneous cell segmentation and tracking which predicts easy to interpret embeddings. As embeddings, offsets of cell pixels to their cell center and bandwidths are learned. We benchmark our approach on nine 2D data sets from the Cell Tracking Challenge, where our approach performs on seven out of nine data sets within the top 3 contestants including three top 1 performances. The source code is publicly available at https://git.scc.kit.edu/kit-loe-ge/embedtrack.