Fan Yi-Ling, Hsu Ching-Han, Hsu Fang-Rong, Liao Lun-De
Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan.
Department of Biomedical Engineering and Environmental Sciences, National Tsing-Hua University, Hsinchu, Taiwan.
Front Bioeng Biotechnol. 2024 Sep 25;12:1461264. doi: 10.3389/fbioe.2024.1461264. eCollection 2024.
Zebrafish are ideal model organisms for various fields of biological research, including genetics, neural transmission patterns, disease and drug testing, and heart disease studies, because of their unique ability to regenerate cardiac muscle. Tracking zebrafish trajectories is essential for understanding their behavior, physiological states, and disease associations. While 2D tracking methods are limited, 3D tracking provides more accurate descriptions of their movements, leading to a comprehensive understanding of their behavior. In this study, we used deep learning models to track the 3D movements of zebrafish. Videos were captured by two custom-made cameras, and 21,360 images were labeled for the dataset. The YOLOv7 model was trained using hyperparameter tuning, with the top- and side-view camera models trained using the v7x.pt and v7.pt weights, respectively, over 300 iterations with 10,680 data points each. The models achieved impressive results, with an accuracy of 98.7% and a recall of 98.1% based on the test set. The collected data were also used to generate dynamic 3D trajectories. Based on a test set with 3,632 3D coordinates, the final model detected 173.11% more coordinates than the initial model. Compared to the ground truth, the maximum and minimum errors decreased by 97.39% and 86.36%, respectively, and the average error decreased by 90.5%.This study presents a feasible 3D tracking method for zebrafish trajectories. The results can be used for further analysis of movement-related behavioral data, contributing to experimental research utilizing zebrafish.
斑马鱼是生物学研究各个领域的理想模式生物，包括遗传学、神经传递模式、疾病与药物测试以及心脏病研究，因为它们具有独特的心肌再生能力。追踪斑马鱼的轨迹对于理解它们的行为、生理状态以及疾病关联至关重要。虽然二维追踪方法存在局限性，但三维追踪能更准确地描述它们的运动，从而全面了解它们的行为。在本研究中，我们使用深度学习模型来追踪斑马鱼的三维运动。视频由两个定制相机拍摄，并为数据集标注了21360张图像。YOLOv7模型通过超参数调整进行训练，顶视图和侧视图相机模型分别使用v7x.pt和v7.pt权重进行训练，在300次迭代中，每次使用10680个数据点。这些模型取得了令人印象深刻的结果，基于测试集的准确率为98.7%，召回率为98.1%。收集到的数据还用于生成动态三维轨迹。基于一个包含3632个三维坐标的测试集，最终模型检测到的坐标比初始模型多173.11%。与真实值相比，最大误差和最小误差分别降低了97.39%和86.36%，平均误差降低了90.5%。本研究提出了一种用于斑马鱼轨迹的可行三维追踪方法。研究结果可用于进一步分析与运动相关的行为数据，为利用斑马鱼的实验研究做出贡献。
