基于优化快速椭圆拟合的二维超声图像胎儿头围自动测量

Devi T. Avalokita, Tessya Rismonita, A. Handayani, A. W. Setiawan
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引用次数: 3

摘要

胎龄(GA)监测胎儿超声成像是一种方法,以观察产前危险因素和准备早期治疗新生儿问题。超声图像中有几个参数可用于估计GA,其中一个是胎儿头围(HC)。然而,胎儿HC测量容易出错,因为它依赖于超声医师或产科医生的手工注释。本研究旨在设计一种基于优化椭圆拟合的算法,在预先定义为胎儿头部候选区域的局部感兴趣区域(RoI)上自动计算胎儿HC。我们的优化方法包括预处理步骤,以排除RoI内的噪声,并选择胎儿头部像素的最佳表示,然后通过椭圆拟合算法进行处理。我们成功地对699和141张分别代表妊娠中期和晚期的超声图像进行椭圆拟合;平均骰子相似系数(DSC)为95.27%±6.25%,豪斯多夫距离(HD)为3.51 mm±5.54 mm,胎儿HC (DF)差异为-3.42 mm±13.66 mm,胎儿HC (ADF)绝对差异为6.53 mm±12.5 mm。结果表明,该方法的性能与文献中发表的其他系统相当。此外,与之前的大多数研究相比,我们的结果代表了对大量数据的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Fetal Head Circumference Measurement in 2D Ultrasound Images Based On Optimized Fast Ellipse Fitting
Gestational age (GA) monitoring from fetal ultrasound imaging is one method to observe pre-birth risk factors and to prepare early treatment for neonatal problems. There are several parameters in an ultrasound image that can be used to estimate GA, one of which is the fetal head circumference (HC). However, fetal HC measurement is prone to error since it relies on manual annotation by sonographer or obstetrician. This research aims to design an algorithm to automatically calculate the fetal HC based on optimized ellipse fitting on a localized region of interest (RoI) previously defined as fetal head candidate area. Our optimization method consists of pre-processing steps to exclude noise within the RoI and to select the optimum representation of fetal head pixels to be processed by the ellipse fitting algorithm. We managed to perform ellipse fitting on 699 and 141 ultrasound images representing respectively the second and third trimester pregnancies; with the average dice similarity coefficient (DSC) of 95.27%±6.25%, hausdorff distance (HD) of 3.51 mm±5.54 mm, a difference in fetal HC (DF) of -3.42 mm±13.66 mm, and an absolute difference in fetal HC (ADF) of 6.53 mm±12.5 mm. The results demonstrated that the presented method performed comparably to other systems published in the literature. Moreover, our results represent an evaluation of a significantly larger number of data compared to most of the previous works.
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