EMBRACE: A Multi-task Framework for Comprehensive Quality Assessment in Cleavage-stage Embryo
2026-07-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed EMBRACE, a deep learning system that helps evaluate early-stage embryo images used in IVF. This system can identify fragmented parts of the embryo, determine its developmental stage, and judge how symmetrical the embryo cells are, all from a single picture. EMBRACE was tested on over 9,000 images and showed high accuracy in all these tasks. The authors suggest this approach can help standardize embryo assessments, but it needs further testing before being used in clinics.
cleavage-stage embryocytoplasmic fragmentationblastomere symmetrydeep learningResNet-50U-Netimage segmentationdevelopmental-stage classificationmacro-F1 scorequadratic weighted kappa
Authors
Anwar Hussain Sofi, Jung-Hua Wang, Ming-Jer Chen, Tsung-Hsien Lee, Yu-Chiao Yi, Ming-Kuan Lin, Yi-Chung Lai
Abstract
Cleavage-stage embryo assessment in in vitro fertilization requires the integrated interpretation of cytoplasmic fragmentation, developmental stage, and blastomere symmetry. However, conventional visual assessment is affected by observer variability, particularly when fragmented regions are small, irregular, or low contrast. This study presents EMBRACE, a multi-task deep learning framework for jointly performing cytoplasmic-fragmentation segmentation, t2/t4 developmental-stage classification, and blastomere-symmetry grading from static cleavage-stage embryo microscopy images. EMBRACE combines a shared ResNet-50 backbone, a concatenation-based multi-scale feature-fusion (C-MSFF) module, a U-Net-style segmentation decoder, and two task-specific classification heads. After predefined inclusion and exclusion criteria, 9,137 annotated embryo images were divided into 7,309 training, 914 validation, and 914 held-out test images. On the held-out test set, EMBRACE achieved a Dice coefficient of 0.781 and an intersection over union of 0.677 for fragmentation segmentation. Developmental-stage classification achieved an accuracy of 0.995, macro-F1 of 0.994, and AUC of 1.000. Blastomere-symmetry grading achieved a balanced accuracy of 0.901, macro-F1 of 0.907, and quadratic weighted kappa of 0.859. These findings support the feasibility of combining spatially inspectable fragmentation localization with embryo-level morphology assessment in a single framework. External and prospective validation is required before clinical deployment.