dorsal/arxiv
View SchemaCell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy
| Authors | Raffaella Fiamma Cabini, Deborah Barkauskas, Guangyu Chen, Zhi-Qi Cheng, David E Cicchetti, Judith Drazba, Rodrigo Fernandez-Gonzalez, Raymond Hawkins, Yujia Hu, Jyoti Kini, Charles LeWarne, Xufeng Lin, Sai Preethi Nakkina, John W Peterson, Koert Schreurs, Ayushi Singh, Kumaran Bala Kandan Viswanathan, Inge MN Wortel, Sanjian Zhang, Rolf Krause, Santiago Fernandez Gonzalez, Diego Ulisse Pizzagalli |
|---|---|
| Categories | |
| ArXiv ID | 2601.10250vv1 |
| URL | https://arxiv.org/abs/2601.10250 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"date_created": "2026-02-17T05:53:24.218000Z",
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"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.\n 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.\n 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.",
"arxiv_id": "2601.10250",
"authors": [
"Raffaella Fiamma Cabini",
"Deborah Barkauskas",
"Guangyu Chen",
"Zhi-Qi Cheng",
"David E Cicchetti",
"Judith Drazba",
"Rodrigo Fernandez-Gonzalez",
"Raymond Hawkins",
"Yujia Hu",
"Jyoti Kini",
"Charles LeWarne",
"Xufeng Lin",
"Sai Preethi Nakkina",
"John W Peterson",
"Koert Schreurs",
"Ayushi Singh",
"Kumaran Bala Kandan Viswanathan",
"Inge MN Wortel",
"Sanjian Zhang",
"Rolf Krause",
"Santiago Fernandez Gonzalez",
"Diego Ulisse Pizzagalli"
],
"categories": [
"eess.IV",
"cs.CV",
"q-bio.QM"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy",
"url": "https://arxiv.org/abs/2601.10250",
"version": "v1"
},
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"variant": "snapshot-2026-01-17",
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