Trophectoderm segmentation in human embryo images via inceptioned U-Net

Area of Science:

• Embryology
• Medical Imaging
• Artificial Intelligence

Background:

• Trophectoderm (TE) segmentation in human blastocysts is vital for assessing embryo quality.
• Accurate TE segmentation is challenging due to limited data and complex morphology.
• Previous automated segmentation methods for TE are scarce.

Purpose of the Study:

• To develop and evaluate deep learning models for precise trophectoderm segmentation in human blastocyst images.
• To enhance the accuracy and generalization of automated embryo quality assessment.
• To address the data scarcity issue in training deep learning models for embryology applications.

Main Methods:

• Proposed four fully convolutional deep learning models for trophectoderm segmentation.
• Developed a multi-scaled ensembling method aggregating five models for improved spatial information.
• Generated synthetic human embryo images to augment training datasets.
• Utilized metrics including Precision, Recall, Accuracy, Dice Coefficient, and Jaccard Index for evaluation.

Main Results:

• The proposed models achieved high segmentation performance: 83.8% Precision, 90.1% Recall, 96.9% Accuracy, 86.61% Dice Coefficient, and 76.71% Jaccard Index.
• The Inceptioned U-Net model demonstrated superior performance over state-of-the-art methods, with significant improvements in Accuracy, Dice Coefficient, and Jaccard Index.
• Synthetic data generation effectively mitigated the generalization gap caused by limited training data.

Conclusions:

• The developed deep learning models offer a robust solution for automated trophectoderm segmentation in human blastocysts.
• The multi-scaled ensembling and synthetic data generation strategies enhance model accuracy and applicability.
• This work represents a significant advancement in automated human embryo quality assessment using morphological features.