AI 中文总结
研究针对卵裂期胚胎评估受观察者差异影响的问题，提出EMBRACE多任务深度学习框架，能联合进行细胞质碎片化分割、发育阶段分类和卵裂球对称性分级，在测试集上取得较好结果，支持单一框架结合多种评估的可行性。
AI 中文摘要
体外受精中卵裂期胚胎评估需要综合解读细胞质碎片化、发育阶段和卵裂球对称性。然而，传统视觉评估受观察者差异影响，尤其是碎片化区域小、不规则或对比度低时。本研究提出EMBRACE，一个多任务深度学习框架，可从静态卵裂期胚胎显微镜图像联合进行细胞质碎片化分割、t2/t4发育阶段分类和卵裂球对称性分级。EMBRACE结合共享的ResNet-50主干、基于拼接的多尺度特征融合模块、U-Net风格分割解码器和两个特定任务分类头。经预定义标准划分，9137张带注释胚胎图像分为训练、验证和测试集。在测试集上，EMBRACE在碎片化分割、发育阶段分类和卵裂球对称性分级上取得了较好结果，支持了在单一框架中结合空间可检查的碎片化定位与胚胎水平形态评估的可行性。临床部署前需外部和前瞻性验证。
英文摘要
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.
