构建仿真框架，评估活体动物神经元追踪算法性能
SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals
- 用可变形背景模拟活体动物运动，生成带标注的合成视频
- 在水螅等生物动态场景中验证算法，揭示现有方法局限性
- 适合神经科学与计算机视觉交叉研究者参考
在行为动物中准确追踪神经元活动面临复杂运动和背景噪声的挑战，且缺乏标注数据限制了追踪算法的评估与改进。为此，我们开发了SINETRA，一个灵活的仿真框架，能够生成粒子在可变形背景上的合成追踪数据，高度模拟真实动物记录中的复杂运动。该框架生成包含2D和3D标注的视频，反映如Hydra Vulgaris等生物在自然行为中的精细动态。我们评估了四种前沿追踪算法，揭示其在复杂场景下的性能瓶颈，为未来动态生物系统中的细胞追踪技术发展提供方向。
原文摘要 · Abstract (English)
Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the evaluation and improvement of such tracking algorithms. To address this, we developed SINETRA, a versatile simulator that generates synthetic tracking data for particles on a deformable background, closely mimicking live animal recordings. This simulator produces annotated 2D and 3D videos that reflect the intricate movements seen in behaving animals like Hydra Vulgaris. We evaluated four state-of-the-art tracking algorithms highlighting the current limitations of these methods in challenging scenarios and paving the way for improved cell tracking techniques in dynamic biological systems.
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