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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2601.10250 |
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| _version_ | 1866909991372324864 |
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| author | Cabini, Raffaella Fiamma Barkauskas, Deborah Chen, Guangyu Cheng, Zhi-Qi Cicchetti, David E Drazba, Judith Fernandez-Gonzalez, Rodrigo Hawkins, Raymond Hu, Yujia Kini, Jyoti LeWarne, Charles Lin, Xufeng Nakkina, Sai Preethi Peterson, John W Schreurs, Koert Singh, Ayushi Viswanathan, Kumaran Bala Kandan Wortel, Inge MN Zhang, Sanjian Krause, Rolf Gonzalez, Santiago Fernandez Pizzagalli, Diego Ulisse |
| author_facet | Cabini, Raffaella Fiamma Barkauskas, Deborah Chen, Guangyu Cheng, Zhi-Qi Cicchetti, David E Drazba, Judith Fernandez-Gonzalez, Rodrigo Hawkins, Raymond Hu, Yujia Kini, Jyoti LeWarne, Charles Lin, Xufeng Nakkina, Sai Preethi Peterson, John W Schreurs, Koert Singh, Ayushi Viswanathan, Kumaran Bala Kandan Wortel, Inge MN Zhang, Sanjian Krause, Rolf Gonzalez, Santiago Fernandez Pizzagalli, Diego Ulisse |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10250 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy Cabini, Raffaella Fiamma Barkauskas, Deborah Chen, Guangyu Cheng, Zhi-Qi Cicchetti, David E Drazba, Judith Fernandez-Gonzalez, Rodrigo Hawkins, Raymond Hu, Yujia Kini, Jyoti LeWarne, Charles Lin, Xufeng Nakkina, Sai Preethi Peterson, John W Schreurs, Koert Singh, Ayushi Viswanathan, Kumaran Bala Kandan Wortel, Inge MN Zhang, Sanjian Krause, Rolf Gonzalez, Santiago Fernandez Pizzagalli, Diego Ulisse Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods 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. |
| title | Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2601.10250 |