Cumulus expansion is an important indicator of oocyte maturation, often correlated with greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. Here, the reliability of three cumulus expansion measurement methods was evaluated and a deep learning model was created to automatically perform the measurement. Cumulus-oocyte complexes were compared before and after in vitro maturation by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. Inter- and intra-observer agreements were calculated using intraclass-correlation coefficients (ICC). The area method resulted in the best overall agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring method, respectively. Therefore, the area method served as the base to create the deep learning model, which outperformed two observers while equivalent to the third. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level accuracy and could therefore be a valuable prospective tool for embryologists.

The cumulus-oocyte complex (COC) is an oocyte surrounded by specialized granulosa cells, called cumulus cells. The cumulus cells surrounding the oocyte ensure healthy oocyte and embryo development. The maturity of COCs at oocyte retrieval may be used as an indicator to predict outcome of assisted reproductive technology (ART). Segmenting COCs is a preliminary step in many image processing pipelines to evaluate maturity. However, acquiring well-annotated bright-field microscopy image datasets remains a time-consuming and inaccurate procedure, for most biological domains. Additionally, specialists often partially disagree on their annotations, not only among each other, but also among their own annotations, leading to an inconsistent outcome. Despite the recent advancements in deep learning and image segmentation tools for biological and biomedical images, there is limited usage of them for having more accurate and automated procedures. In this work, we propose an automated pipeline to segment bovine COCs in bright-field microscopy images. The results of our evaluation show that our pipeline is able to segment COCs with the same level of quality as provided by human experts.