构建细胞行为视频分类基准，推动显微镜视频分析发展
Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy
- 用追踪特征+端到端深度学习+融合方法评测35种模型
- 端到端方法在完整序列建模上表现更优，提升分类准确率
- 适合生物图像分析、计算机视觉研究者参考
捕捉复杂细胞行为的显微镜视频分类对理解生物过程动态至关重要。然而，这仍是计算机视觉的前沿挑战，需要能有效建模无固定边界的物体形状与运动、从整个图像序列中提取分层时空特征，并处理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），基于三种方法对比了35种模型：基于追踪特征的分类、直接从完整视频序列中端到端学习时空特征的深度网络，以及融合追踪与图像特征的方法。我们分析了参赛者结果，比较了各方法的潜力与局限，为推动计算机视觉在细胞动力学研究中的应用提供基础。
原文摘要 · Abstract (English)
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.
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