From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914579107282944 |
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| author | Hachani, Yasmine Bouthemy, Patrick Fromont, Elisa Duranthon, Véronique Laffont, Ludivine Reis, Alline de Paula |
| author_facet | Hachani, Yasmine Bouthemy, Patrick Fromont, Elisa Duranthon, Véronique Laffont, Ludivine Reis, Alline de Paula |
| contents | Accurate selection of bovine embryos is a challenging task, as current practice relies on a single expert assessment on the seventh day after insemination, resulting in high rates of pregnancy loss. Time-lapse videomicroscopy provides detailed information on early development, but is difficult to exploit because of complex motion patterns and time-consuming analysis. We propose TransFACT, a transformer-based framework for modeling early developmental stages and embryo transferability using 2D time-lapse videos from the first four days of development. TransFACT combines frame-level temporal features with stage-level representations, using developmental stages as auxiliary supervision to predict transferability on day four. Our experiments demonstrate that TransFACT, by leveraging an existing method designed for action recognition, achieves superior performance than its competitor in predicting embryo transferability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_18923 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction Hachani, Yasmine Bouthemy, Patrick Fromont, Elisa Duranthon, Véronique Laffont, Ludivine Reis, Alline de Paula Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods Accurate selection of bovine embryos is a challenging task, as current practice relies on a single expert assessment on the seventh day after insemination, resulting in high rates of pregnancy loss. Time-lapse videomicroscopy provides detailed information on early development, but is difficult to exploit because of complex motion patterns and time-consuming analysis. We propose TransFACT, a transformer-based framework for modeling early developmental stages and embryo transferability using 2D time-lapse videos from the first four days of development. TransFACT combines frame-level temporal features with stage-level representations, using developmental stages as auxiliary supervision to predict transferability on day four. Our experiments demonstrate that TransFACT, by leveraging an existing method designed for action recognition, achieves superior performance than its competitor in predicting embryo transferability. |
| title | From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2605.18923 |