Zebrafish Counting Using Event Stream Data

2025-04-18

AI Summary

To address the challenges of manual counting of zebrafish—owing to their small size—and the low accuracy and poor robustness of existing methods in dense, small-object scenarios, this paper pioneers the integration of event cameras into automated zebrafish counting. Our approach comprises camera calibration, multi-frame event image fusion, motion trajectory reconstruction, and sliding-time-window statistics, augmented by trajectory modeling and ceiling-based decision making to enable real-time, low-complexity counting at high temporal resolution. We conduct 100 independent trials in a 4-L tank containing 20 zebrafish; the method achieves a mean counting accuracy of 97.95%, significantly outperforming conventional frame-based imaging approaches. This work establishes a novel paradigm for dynamic counting of microscopic biological organisms, offering high precision, strong robustness against occlusion and motion blur, and practical deployability in laboratory settings.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Zebrafish share a high degree of homology with human genes and are commonly used as model organism in biomedical research. For medical laboratories, counting zebrafish is a daily task. Due to the tiny size of zebrafish, manual visual counting is challenging. Existing counting methods are either not applicable to small fishes or have too many limitations. The paper proposed a zebrafish counting algorithm based on the event stream data. Firstly, an event camera is applied for data acquisition. Secondly, camera calibration and image fusion were preformed successively. Then, the trajectory information was used to improve the counting accuracy. Finally, the counting results were averaged over an empirical of period and rounded up to get the final results. To evaluate the accuracy of the algorithm, 20 zebrafish were put in a four-liter breeding tank. Among 100 counting trials, the average accuracy reached 97.95%. As compared with traditional algorithms, the proposed one offers a simpler implementation and achieves higher accuracy.

Problem

• Develops zebrafish counting using event stream data
• Improves accuracy via trajectory information fusion
• Achieves 97.95% accuracy in experimental trials

Innovation

• Uses event camera for zebrafish data acquisition
• Improves counting accuracy with trajectory information
• Averages results over empirical period for precision