用深度学习自动估胎龄，提升早产儿评估准确性。
AGE-US: automated gestational age estimation based on fetal ultrasound images
- 设计新分割架构结合距离图，实现无须大量标注的胎龄估算。
- 在有限数据下达到顶尖模型性能，误差接近人工测量。
- 特别适合医疗资源少、标注数据稀缺的场景使用。
早产或低体重儿面临更高的新生儿死亡率及未来心脏病风险。准确估算胎龄对监测胎儿发育至关重要，但传统方法（如末次月经推算）在某些情况下难以获取。超声波方法虽更可靠，却依赖人工测量，易引入偏差。本文提出一种可解释的深度学习方法，基于新型分割架构与距离图，克服数据集小和标注掩码稀缺的问题。该方法性能媲美当前最优模型，同时降低复杂度，特别适用于资源有限、标注数据不足的环境。此外，实验表明距离图在估计股骨末端时表现尤为出色。
原文摘要 · Abstract (English)
Being born small carries significant health risks, including increased neonatal mortality and a higher likelihood of future cardiac diseases. Accurate estimation of gestational age is critical for monitoring fetal growth, but traditional methods, such as estimation based on the last menstrual period, are in some situations difficult to obtain. While ultrasound-based approaches offer greater reliability, they rely on manual measurements that introduce variability. This study presents an interpretable deep learning-based method for automated gestational age calculation, leveraging a novel segmentation architecture and distance maps to overcome dataset limitations and the scarcity of segmentation masks. Our approach achieves performance comparable to state-of-the-art models while reducing complexity, making it particularly suitable for resource-constrained settings and with limited annotated data. Furthermore, our results demonstrate that the use of distance maps is particularly suitable for estimating femur endpoints.
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