用有限影像预测胚胎能否发育成囊胚，准确率达96.4%
Predicting Blastocyst Formation in IVF: Integrating DINOv2 and Attention-Based LSTM on Time-Lapse Embryo Images
- 结合DINOv2与带注意力机制的LSTM，从每日图像中提取发育特征
- 在704个胚胎数据上达到96.4%准确率，优于现有方法
- 能处理缺失帧，适合缺乏完整时间序列设备的诊所使用
体外受精（IVF）中选择最佳胚胎进行移植是关键但极具挑战性的步骤，主要因依赖对大量时间序列影像的人工分析。核心难题是从有限的每日影像中预测胚胎是否能形成囊胚，且许多诊所缺乏完整的时序成像系统，全视频常不可得。本研究旨在利用有限的每日时间序列图像预测胚胎能否发育为囊胚。提出一种新型混合模型，结合基于Transformer的视觉模型DINOv2与改进的多头注意力长短期记忆网络（LSTM）。DINOv2从胚胎图像中提取有意义特征，LSTM则利用这些特征分析胚胎发育动态并生成最终预测。在包含704个胚胎视频的真实数据集上测试，模型达到96.4%准确率，显著优于现有方法，并具备良好的缺帧容错能力，对配备不完整成像系统的IVF实验室具有实际价值。该方法可帮助胚胎学家更高效、更可靠地筛选优质胚胎。
原文摘要 · Abstract (English)
The selection of the optimal embryo for transfer is a critical yet challenging step in in vitro fertilization (IVF), primarily due to its reliance on the manual inspection of extensive time-lapse imaging data. A key obstacle in this process is predicting blastocyst formation from the limited number of daily images available. Many clinics also lack complete time-lapse systems, so full videos are often unavailable. In this study, we aimed to predict which embryos will develop into blastocysts using limited daily images from time-lapse recordings. We propose a novel hybrid model that combines DINOv2, a transformer-based vision model, with an enhanced long short-term memory (LSTM) network featuring a multi-head attention layer. DINOv2 extracts meaningful features from embryo images, and the LSTM model then uses these features to analyze embryo development over time and generate final predictions. We tested our model on a real dataset of 704 embryo videos. The model achieved 96.4% accuracy, surpassing existing methods. It also performs well with missing frames, making it valuable for many IVF laboratories with limited imaging systems. Our approach can assist embryologists in selecting better embryos more efficiently and with greater confidence.
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。