Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inaccurate, and landmark detection requires developing measurement-specific geometric methods. This paper describes BiometryNet, an end-to-end landmark regression framework for fetal biometry estimation that overcomes these limitations. It includes a novel Dynamic Orientation Determination (DOD) method for enforcing measurement-specific orientation consistency during network training. DOD reduces variabilities in network training, increases landmark localization accuracy, thus yields accurate and robust biometric measurements. To validate our method, we assembled a dataset of 3,398 ultrasound images from 1,829 subjects acquired in three clinical sites with seven different ultrasound devices. Comparison and cross-validation of three different biometric measurements on two independent datasets shows that BiometryNet is robust and yields accurate measurements whose errors are lower than the clinically permissible errors, outperforming other existing automated biometry estimation methods. Code is available at https://github.com/netanellavisdris/fetalbiometry.
翻译:超声波的胚胎生长评估所依据的是人工进行的、与预期的妊娠年龄相比评估的一些生物测定测量数据。可靠的生物测量估计取决于准确探测标准超声波飞机的里程碑。人工注解可以是耗时和操作者依赖的任务,并可能导致高测量变异性。现有的胚胎生物测定方法依赖于初始自动自动胎儿结构分解,然后是几何地标检测。然而,分解说明耗费时间,可能不准确,而里程碑式检测需要制定具体的测量方法。本文描述生物测量网,这是为克服这些限制的胎儿生物测量估计而建立的端至端里程碑式回归框架。其中包括在网络培训期间执行具体测量方向一致性的新的动态方向确定(DODD)方法。DO减少网络培训中的变异性,提高地标定位准确度,从而实现准确和可靠的生物测量测量测量。为了验证我们的方法,我们收集了在三个临床地点获得的1,829个主题的3,398贝氏超声谱图像数据集。在进行对比和交叉对比时,现有三种不同生物测定模型的精确度测量方法显示,其现有两种不同生物测定方法的精确度的精确度测量方法是现有两种不同的生物测量方法。