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.

翻译：胎儿出生时体型过小会带来显著的健康风险，包括新生儿死亡率升高以及未来罹患心脏疾病的可能性增加。准确估计孕周对于监测胎儿生长至关重要，但传统方法（例如基于末次月经的估计）在某些情况下难以获取。尽管基于超声的方法提供了更高的可靠性，但它们依赖于人工测量，这引入了变异性。本研究提出了一种基于可解释深度学习的自动孕周计算方法，利用新颖的分割架构和距离图来克服数据集限制和分割掩码稀缺的问题。我们的方法在降低复杂度的同时实现了与最先进模型相当的性能，使其特别适用于资源受限且标注数据有限的环境。此外，我们的结果表明，距离图的使用特别适用于股骨端点的估计。