一个模型同时完成胚胎分割、评级和着床预测，提升试管婴儿评估效率。
Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction
- 用多任务学习统一处理胚胎结构分割、形态评级与着床预测。
- 在公开数据集上实现ICM、ZP、TE分割Dice值分别达94.93%、91.60%、88.82%，着床预测F1为80.0%。
- 通过Grad-CAM++可视化确保结果符合解剖结构，适合临床辅助决策场景。
本研究提出Blasto-Net，一种用于全面胚胎分析的多任务深度学习模型。该模型在一次前向传播中同时完成透明带（ZP）、滋养外胚层（TE）和内细胞团（ICM）的分割、形态学评级及着床结果预测。体外受精（IVF）中的胚胎分析极具挑战性，因各组织区域纹理相似但结构差异大。为此，Blasto-Net采用EfficientNet-B3编码器结合带有卷积块注意力模块（CBAM）和新型边缘感知注意力模块（EAAM）的UNet式解码器，有效捕捉语义与边界信息。针对不同区域拓扑特性，模型使用专用分割头并引入基于区域与边界的复合损失函数。此外，通过Grad-CAM++可视化验证预测结果的解剖一致性。在公开的HMC胚胎数据集上，Blasto-Net在ICM、ZP、TE分割上的Dice分数分别为94.93%、91.60%、88.82%，着床预测的F1得分为80.0%。结果表明，Blasto-Net提供了一种准确、可解释且高效的自动化胚胎评估方案，具有显著支持临床决策的潜力。
原文摘要 · Abstract (English)
This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simultaneously in a single forward pass: segmentation of the ZP, TE, and ICM compartments, morphological grading, and implantation outcome prediction. Accurate blastocyst analysis in in vitro fertilization (IVF) is challenging. The compartments often have similar textures but very different structures. To address these challenges, Blasto-Net employs an EfficientNet-B3 encoder with a UNet-style decoder enhanced by the Convolutional Block Attention Module (CBAM) and a novel Edge-Aware Attention Module (EAAM) to effectively capture both semantic and boundary information. To handle distinct compartment topologies, the network employs specialized segmentation heads and a composite region- and boundary-based loss. Additionally, Grad-CAM++ visualizations are used to verify the anatomical consistency of the model's predictions. Evaluated on a public HMC blastocyst dataset, Blasto-Net achieves Dice scores of 94.93%, 91.60%, and 88.82% for ICM, ZP, and TE, respectively, alongside an implantation F1-score of 80.0%. These results demonstrate that Blasto-Net offers an accurate, interpretable, and efficient solution for automated blastocyst assessment, with strong potential to support clinical decision-making in IVF.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。