Supervised contrastive learning for cell stage classification of animal embryos
Journal:
arXiv
Published Date:
Feb 11, 2025
Abstract
Video microscopy, when combined with machine learning, offers a promising
approach for studying the early development of in vitro produced (IVP) embryos.
However, manually annotating developmental events, and more specifically cell
divisions, is time-consuming for a biologist and cannot scale up for practical
applications. We aim to automatically classify the cell stages of embryos from
2D time-lapse microscopy videos with a deep learning approach. We focus on the
analysis of bovine embryonic development using video microscopy, as we are
primarily interested in the application of cattle breeding, and we have created
a Bovine Embryos Cell Stages (ECS) dataset. The challenges are three-fold: (1)
low-quality images and bovine dark cells that make the identification of cell
stages difficult, (2) class ambiguity at the boundaries of developmental
stages, and (3) imbalanced data distribution. To address these challenges, we
introduce CLEmbryo, a novel method that leverages supervised contrastive
learning combined with focal loss for training, and the lightweight 3D neural
network CSN-50 as an encoder. We also show that our method generalizes well.
CLEmbryo outperforms state-of-the-art methods on both our Bovine ECS dataset
and the publicly available NYU Mouse Embryos dataset.
