标准平面(SP)对于胎儿脑畸形的诊断至关重要。然而,由于胎儿姿势的巨大差异和SPs定义的复杂性,准确获取SPs非常耗时且需要丰富的经验.
这项研究旨在提供一种指导方法,可以帮助超声医师更准确,更快速地获得SP。
首先,超声医师使用3D探头扫描胎儿头部以获得3D体积数据,然后我们使用仿射变换将3D体数据校准到标准身体位置,并实时建立相应的3D头部模型。当超声医师使用2D探头扫描飞机时,当前平面的位置可以通过我们的RLNet(回归位置网络)在3D头部模型中清楚地显示,这可以指导超声医师更准确地获得三个SP。当找到三个SP时,根据与三个SP的空间关系,可以自动生成矢状面和冠状面。
在3200个2DUS图像上进行的实验结果表明,RLNet实现了跨丘脑平面的平均角度误差为3.91±2.86°,与其他已发表的数据相比,有明显的改善。自动生成的冠状和矢状SP符合胎儿脑畸形的诊断标准和诊断要求。
首次提出了一种基于深度学习的超声脑畸形筛查引导扫描方法,具有一定的临床应用价值。
Standard planes (SPs) are crucial for the diagnosis of fetal brain malformation. However, it is very time-consuming and requires extensive experiences to acquire the SPs accurately due to the large difference in fetal posture and the complexity of SPs definitions.
This study aims to present a guiding approach that could assist sonographer to obtain the SPs more accurately and more quickly.
To begin with, sonographer uses the 3D probe to scan the fetal head to obtain 3D volume data, and then we used affine transformation to calibrate 3D volume data to the standard body position and established the corresponding 3D head model in \'real time\'. When the sonographer uses the 2D probe to scan a plane, the position of current plane can be clearly show in 3D head model by our RLNet (regression location network), which can conduct the sonographer to obtain the three SPs more accurately. When the three SPs are located, the sagittal plane and the coronal planes can be automatically generated according to the spatial relationship with the three SPs.
Experimental results conducted on 3200 2D US images show that the RLNet achieves average angle error of the transthalamic plane was 3.91±2.86°, which has a obvious improvement compared other published data. The automatically generated coronal and sagittal SPs conform the diagnostic criteria and the diagnostic requirements of fetal brain malformation.
A guiding scanning method based deep learning for ultrasonic brain malformation screening is firstly proposed and it has a pragmatic value for future clinical application.