Yaqub Mohammad, Kelly Brenda, Papageorghiou Aris T, Noble J Alison
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Nuffield Department of Obstetrics & Gynaecology, University of Oxford, Oxford, UK.
Ultrasound Med Biol. 2017 Dec;43(12):2925-2933. doi: 10.1016/j.ultrasmedbio.2017.07.013. Epub 2017 Sep 28.
During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images of the fetal brain have been correctly acquired and meet the required clinical standard. We propose a deep learning solution, which breaks the problem down into three stages: (i) accurate localization of the fetal brain, (ii) detection of regions that contain structures of interest and (iii) learning the acoustic patterns in the regions that enable plane verification. We evaluate the developed methodology on a large real-world clinical data set of 2-D mid-gestation fetal images. We show that the automatic verification method approaches human expert assessment.
在对胎儿大脑进行常规超声评估以进行生物测量估计和检测胎儿异常时，超声科医生必须按照明确的成像方案或临床标准找到准确的成像平面，非专业人员很难做好这一点。这种评估有助于提供准确的生物测量估计并检测可能的脑部异常。我们描述了一种机器学习方法，用于自动评估胎儿大脑的经脑室超声图像是否已正确获取并符合所需的临床标准。我们提出了一种深度学习解决方案，该方案将问题分解为三个阶段：（i）胎儿大脑的精确定位，（ii）检测包含感兴趣结构的区域，以及（iii）学习能够进行平面验证的区域中的声学模式。我们在一个包含二维孕中期胎儿图像的大型真实世界临床数据集上评估了所开发的方法。我们表明，自动验证方法接近人类专家评估。
