Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality
This work addresses the need for more objective and standardized embryo selection in IVF, though it is incremental as it applies existing CNN methods to a specific medical imaging task.
The study tackled the problem of subjective and inconsistent manual embryo quality assessment in IVF by evaluating deep convolutional neural networks for classifying embryo images based on morphological quality, finding that Xception performed best among the tested architectures.
A critical factor that influences the success of an in-vitro fertilization (IVF) procedure is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories. Network architectures and hyper-parameters affect the efficiency of CNNs for any given task. Here, we evaluate multi-layered CNNs developed from scratch and popular deep-learning architectures such as Inception v3, ResNET, Inception-ResNET-v2, and Xception in differentiating between embryos based on their morphological quality at 113 hours post insemination (hpi). Xception performed the best in differentiating between the embryos based on their morphological quality.