Early prediction of the transferability of bovine embryos from videomicroscopy
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915102343561216 |
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| author | Hachani, Yasmine Bouthemy, Patrick Fromont, Elisa Ruffini, Sylvie Laffont, Ludivine Reis, Alline de Paula |
| author_facet | Hachani, Yasmine Bouthemy, Patrick Fromont, Elisa Ruffini, Sylvie Laffont, Ludivine Reis, Alline de Paula |
| contents | Videomicroscopy is a promising tool combined with machine learning for studying the early development of in vitro fertilized bovine embryos and assessing its transferability as soon as possible. We aim to predict the embryo transferability within four days at most, taking 2D time-lapse microscopy videos as input. We formulate this problem as a supervised binary classification problem for the classes transferable and not transferable. The challenges are three-fold: 1) poorly discriminating appearance and motion, 2) class ambiguity, 3) small amount of annotated data. We propose a 3D convolutional neural network involving three pathways, which makes it multi-scale in time and able to handle appearance and motion in different ways. For training, we retain the focal loss. Our model, named SFR, compares favorably to other methods. Experiments demonstrate its effectiveness and accuracy for our challenging biological task. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_07945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Early prediction of the transferability of bovine embryos from videomicroscopy Hachani, Yasmine Bouthemy, Patrick Fromont, Elisa Ruffini, Sylvie Laffont, Ludivine Reis, Alline de Paula Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Quantitative Methods Videomicroscopy is a promising tool combined with machine learning for studying the early development of in vitro fertilized bovine embryos and assessing its transferability as soon as possible. We aim to predict the embryo transferability within four days at most, taking 2D time-lapse microscopy videos as input. We formulate this problem as a supervised binary classification problem for the classes transferable and not transferable. The challenges are three-fold: 1) poorly discriminating appearance and motion, 2) class ambiguity, 3) small amount of annotated data. We propose a 3D convolutional neural network involving three pathways, which makes it multi-scale in time and able to handle appearance and motion in different ways. For training, we retain the focal loss. Our model, named SFR, compares favorably to other methods. Experiments demonstrate its effectiveness and accuracy for our challenging biological task. |
| title | Early prediction of the transferability of bovine embryos from videomicroscopy |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2501.07945 |