# TransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals

arXiv: https://arxiv.org/abs/2512.01885 · HTML: https://mlsanity.com/paper/2512.01885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking](https://mlsanity.com/paper/2211.08824/) (2022)
- [Graph Neural Network for Cell Tracking in Microscopy Videos](https://mlsanity.com/paper/2202.04731/) (2022)
- [Trackastra: Transformer-based cell tracking for live-cell microscopy](https://mlsanity.com/paper/2405.15700/) (2024)
- [Cell Tracking via Proposal Generation and Selection](https://mlsanity.com/paper/1705.03386/) (2017)
- [Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art](https://mlsanity.com/paper/2506.13457/) (2025)
- [Multi-object Tracking with Tracked Object Bounding Box Association](https://mlsanity.com/paper/2105.07901/) (2021)
- [RLM-Tracking: Online Multi-Pedestrian Tracking Supported by Relative Location Mapping](https://mlsanity.com/paper/2210.10477/) (2022)

## Built on by (most cited first, 1 of 1)

- [Understanding Cell Fate Decisions with Temporal Attention](https://mlsanity.com/paper/2603.16562/) (2026)

## Abstract (arXiv)

Tracking cells in time-lapse videos is an essential technique for monitoring cell population dynamics at a single-cell level. Current methods for cell tracking are developed on videos with mostly single, constant signals and do not detect pivotal events such as cell death. Here, we present TransientTrack, a deep learning-based framework for cell tracking in multi-channel microscopy video data with transient fluorescent signals that fluctuate over time following processes such as the circadian rhythm of cells. By identifying key cellular events - mitosis (cell division) and apoptosis (cell death) our method allows us to build complete trajectories, including cell lineage information. TransientTrack is lightweight and performs matching on cell detection embeddings directly, without the need for quantification of tracking-specific cell features. Furthermore, our approach integrates Transformer Networks, multi-stage matching using all detection boxes, and the interpolation of missing tracklets with the Kalman Filter. This unified framework achieves strong performance across diverse conditions, effectively tracking cells and capturing cell division and death. We demonstrate the use of TransientTrack in an analysis of the efficacy of a chemotherapeutic drug at a single-cell level. The proposed framework could further advance quantitative studies of cancer cell dynamics, enabling detailed characterization of treatment response and resistance mechanisms. The code is available at https://github.com/bozeklab/TransientTrack.