ArticleFrontiers in bioengineering and biotechnology2020
Deep Reinforcement Learning for Data Association in Cell Tracking.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- IEGS-BoT: An Integrated Detection-Tracking Framework for Cellular Dynamics Analysis in Medical Imaging.Biomimetics (Basel, Switzerland) · 2025Article
- Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells.PLoS computational biology · 2025Article
- DeepKymoTracker: A tool for accurate construction of cell lineage trees for highly motile cells.PloS one · 2025Article
- A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.International journal of molecular sciences · 2023Article
- Quantitative Approaches to Study Retinal Neurogenesis.Biomedicines · 2021Review
- Review
- AutomatedFrontiers in cellular neuroscience · 2021Article
- Multi-Target Coordinated Search Algorithm for Swarm Robotics Considering Practical Constraints.Frontiers in neurorobotics · 2021Article
- Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter.Sensors (Basel, Switzerland) · 2020Article
- A bird's-eye view of deep learning in bioimage analysis.Computational and structural biotechnology journal · 2020Review
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Authors and funding
4 authors.
Funding
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Abstract
Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells. We propose a deep reinforcement learning method to associate the detected targets between frames. According to the dynamic model of each target, the cost matrix is produced by conjointly considering various features of targets and then used as the input of a neural network. The proposed neural network is trained using reinforcement learning to predict a distribution over the association solution. Furthermore, we design a residual convolutional neural network that results in more efficient learning. We validate our method on two applications: the multiple target tracking simulation and the ISBI cell tracking. The results demonstrate that our approach based on reinforcement learning techniques could effectively track targets following different motion patterns and show competitive results.
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Registered trials
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