In vitro fertilization (IVF) relies heavily on manual microscopic embryo assessment, which is time-consuming, prone to error, and requires specialized knowledge. We propose self-supervised learning (SSL) to automate embryo detection and assessment in IVF videos, reducing the need for manual annotation. Our approach leverages the inherent structure of embryo development data to train models without extensive human labeling. Using a dataset of 176 day-3 and 186 day-5 embryo videos, we demonstrate the potential of SSL in improving the accuracy and consistency of embryo segmentation. While initial results show a mean Intersection over Union (IoU) of 0.55, the removal of data with ambiguous features significantly improves performance, achieving a mean IoU of 0.70. We also compare our SSL model with the YOLOv8 object detection architecture, highlighting the promise of SSL for embryo assessment, especially with further optimization. Our findings demonstrate that SSL can revolutionize IVF embryo segmentation by not only enhancing efficiency and accuracy but also reducing the dependency on manual annotations, which allows for scalability in IVF clinics. Furthermore, this approach fosters collaboration between embryologists and AI engineers, bridging the gap between domain-specific knowledge and automated processes. Our study demonstrates the effectiveness of self-supervised learning (SSL) for biomedical image labeling in IVF. By focusing on the potential of SSL for embryo segmentation, we emphasize its time-saving advantage over traditional supervised learning approaches. Additionally, our approach includes techniques for managing ambiguous embryo features, and we provide a comparative analysis with models like YOLOv8 to validate SSL’s robustness and applicability in clinical settings.