Early prediction of the transferability of bovine embryos from videomicroscopy

• URL: http://arxiv.org/abs/2501.07945v1
• Date: Tue, 14 Jan 2025 08:56:59 GMT
• Title: Early prediction of the transferability of bovine embryos from videomicroscopy
• Authors: Yasmine Hachani, Patrick Bouthemy, Elisa Fromont, Sylvie Ruffini, Ludivine Laffont, Alline de Paula Reis,
• Abstract summary: We aim to predict the embryo transferability within four days at most, taking 2D time-lapse microscopy videos as input. 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.
• Score: 3.4030535409936147
• License:
• Abstract: 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.

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