融合影像特征与深度学习，自动估算胎儿孕周。
Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
- 结合放射组学特征与深度表征，实现无测量的孕周估计
- 跨三孕期平均绝对误差仅8.0天，优于现有方法
- 模型在不同地区人群上表现稳定，适合临床部署
准确的孕周（GA）估算是提供优质产前护理的关键。传统依赖人工超声测量的方法受操作者影响大且耗时。为此，本文提出一种新型特征融合框架，仅使用胎儿超声图像即可估计孕周，无需任何测量数据。采用深度学习模型提取图像深层表征，同时通过放射组学特征揭示胎儿大脑发育的模式与特征。为增强医学影像分析的可解释性，将放射组学特征与深度表征融合以预测孕周。实验结果显示，该框架在三个孕期内均达到8.0天的平均绝对误差，优于当前基于机器学习的方法。结果表明该方法在不同地理区域的人群中具有鲁棒性。代码已公开于https://github.com/13204942/RadiomicsImageFusion_FetalUS。
原文摘要 · Abstract (English)
Accurate gestational age (GA) estimation, ideally through fetal ultrasound measurement, is a crucial aspect of providing excellent antenatal care. However, deriving GA from manual fetal biometric measurements depends on the operator and is time-consuming. Hence, automatic computer-assisted methods are demanded in clinical practice. In this paper, we present a novel feature fusion framework to estimate GA using fetal ultrasound images without any measurement information. We adopt a deep learning model to extract deep representations from ultrasound images. We extract radiomic features to reveal patterns and characteristics of fetal brain growth. To harness the interpretability of radiomics in medical imaging analysis, we estimate GA by fusing radiomic features and deep representations. Our framework estimates GA with a mean absolute error of 8.0 days across three trimesters, outperforming current machine learning-based methods at these gestational ages. Experimental results demonstrate the robustness of our framework across different populations in diverse geographical regions. Our code is publicly available on \href{https://github.com/13204942/RadiomicsImageFusion_FetalUS}.
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