Abstract
The classification of microscopy videos capturing complex cellular behaviors is crucial for understanding and quantifying the dynamics of biological processes over time. However, it remains a frontier in computer vision, requiring approaches that effectively model the shape and motion of objects without rigid boundaries, extract hierarchical spatiotemporal features from entire image sequences rather than static frames, and account for multiple objects within the field of view. To this end, we organized the Cell Behavior Video Classification Challenge (CBVCC), benchmarking 35 methods based on three approaches: classification of tracking-derived features, end-to-end deep learning architectures to directly learn spatiotemporal features from the entire video sequence without explicit cell tracking, or ensembling tracking-derived with image-derived features. We discuss the results achieved by the participants and compare the potential and limitations of each approach, serving as a basis to foster the development of computer vision methods for studying cellular dynamics.
Abstract (translated)
捕捉复杂细胞行为的显微视频分类对于理解并量化生物过程的时间动态至关重要。然而，这仍然是计算机视觉领域的一个前沿问题，需要能够有效建模没有刚性边界的对象形状和运动、从整个图像序列而非静态帧中提取层级时空特征，并考虑视野内多个对象的方法。为此，我们组织了细胞行为视频分类挑战赛（CBVCC），根据三种方法对35种方法进行了基准测试：基于追踪衍生特性的分类、端到端的深度学习架构直接从整个视频序列中学习时空特征而无需显式细胞跟踪，或者结合追踪衍生与图像衍生特性进行集成。我们讨论了参赛者所取得的结果，并比较了每种方法的潜力和局限性，旨在为研究细胞动力学的计算机视觉方法的发展奠定基础。
URL
https://arxiv.org/abs/2601.10250
