Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting
Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting
复制标题
使用随机森林和快速椭圆拟合的超声自动胎头周长测量
DOI：
10.1109/jbhi.2017.2703890
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发表时间：
2018-01-01
影响因子：
7.7
通讯作者：
Ni, Dong
中科院分区：
文献类型：
--
作者：
Li, Jing;Wang, Yi;Ni, Dong
头围（HC）是产前超声检查中评估胎儿生长的最重要的生物特征之一。然而，医生对这种生物特征的手动测量通常需要大量的经验。我们开发了一个基于学习的框架，使用先验知识，并采用快速椭圆拟合方法（ElliFit）来自动测量HC。我们首先将胎龄和超声扫描深度的先验知识整合到随机森林分类器中，以定位胎儿头部。我们进一步使用相位对称性来检测胎儿颅骨的中心线，并使用ElliFit来拟合HC椭圆进行测量。对145幅HC图像的实验结果表明，该方法的平均测量误差为1.7 mm，优于传统方法。实验结果表明，我们的方法在临床实践中的应用显示出很大的希望。
Head circumference (HC) is one of the most important biometrics in assessing fetal growth during prenatal ultrasound examinations. However, the manual measurement of this biometric by doctors often requires substantial experience. We developed a learning-based framework that used prior knowledge and employed a fast ellipse fitting method (ElliFit) to measure HC automatically. We first integrated the prior knowledge about the gestational age and ultrasound scanning depth into a random forest classifier to localize the fetal head. We further used phase symmetry to detect the center line of the fetal skull and employed ElliFit to fit the HC ellipse for measurement. The experimental results from 145 HC images showed that our method had an average measurement error of 1.7 mm and outperformed traditional methods. The experimental results demonstrated that our method shows great promise for applications in clinical practice.