IEEE Trans Med Imaging. 2014 May;33(5):1054-70. doi: 10.1109/TMI.2014.2301936.
Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic measurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.
妊娠第二和第三个三个月的常规超声检查包括在二维扫描中手动测量胎儿头部和大脑结构。该过程需要超声技师使用探头找到标准化的可视化平面，并在手动画出感兴趣结构的测量卡尺。该过程繁琐、耗时且会引入用户测量的变异性。本文提出了一种自动胎儿头部和大脑（AFHB）系统，用于自动从 3D 超声体积中测量解剖结构。该系统在分层分辨率的 3D 体积中进行搜索，并专注于可能是测量解剖结构的区域。输出结果是具有正确定向和居中的平面的标准化可视化以及解剖结构的生物计量测量。该系统基于一种用于在 3D 体积中检测多个结构的新型框架。由于在大多数实际情况下难以获得联合模型，因此按顺序逐个检测结构。检测依赖于序列估计技术，该技术常用于视觉跟踪。结构姿势的相互依赖性和我们领域中嵌入的强先验信息，使得结果比单独检测对象更快、更准确。在每个步骤中，通过顺序蒙特卡罗方法近似结构姿势的后验分布。样本在序列中在多个结构和层次级别之间传播。概率模型有助于解决胎儿超声图像中存在的许多挑战，例如斑点噪声、信号丢失、骨骼引起的阴影以及因胎儿胎龄差异引起的外观变化。这可以通过在包含两千多个体积和超过一万三千个注释的广泛扫描数据库上进行有区别的学习来实现。地面实况和自动测量之间的平均差异小于 2 毫米，运行时间为 6.9 秒（GPU）或 14.7 秒（CPU）。AFHB 系统的准确性在用户间变异性内，运行时间快，满足临床使用的要求。
