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.

翻译：在行为动物中精确追踪神经元活动，由于复杂的运动与背景噪声，带来了重大挑战。缺乏标注数据集限制了此类追踪算法的评估与改进。为此，我们开发了SINETRA，一个多功能模拟器，它能在可变形的背景上生成用于粒子追踪的合成数据，高度模拟活体动物记录。该模拟器生成反映行为动物（如Hydra Vulgaris）中复杂运动的带标注2D与3D视频。我们评估了四种先进的追踪算法，揭示了这些方法在挑战性场景中的当前局限性，并为动态生物系统中改进的细胞追踪技术铺平了道路。