Enhancing Embryo Stage Classification with Multi-Focal Plane Imaging and Deep Learning
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Purpose
Accurately identifying embryo developmental stages is crucial for improving success rates in in vitro fertilization (IVF). Traditional embryo assessment relies on 2D imaging with a single focal plane, which may overlook critical morphological details and lead to misclassification. This study investigates whether incorporating depth information through multi-focal plane imaging can enhance the accuracy of embryo stage classification.
Methods
We compared 2D and 3D convolutional neural network (CNN) architectures trained on a time-lapse embryo dataset containing seven focal planes. The models were evaluated based on their classification performance across different embryo developmental stages. Additionally, we used Gradient-weighted Class Activation Mapping (Grad-CAM) to analyze model attention and interpretability.
Results
The findings demonstrate that 3D CNN models leveraging multi-focal plane data significantly outperform their 2D counterparts, particularly in complex stages such as Morula formation (tM) and expanded blastocyst (tSB, tB, and tEB). Grad-CAM visualization confirms that the models focus on relevant morphological structures, further validating the advantages of volumetric modeling.
Conclusion
Multi-focal plane imaging enhances embryo stage classification accuracy by providing richer morphological information. This study underscores the potential of volumetric modeling in embryo assessment and paves the way for extending this approach to full 3D time-lapse analysis.
