College of Biomedical Engineering, Sichuan University, Chengdu, China.
Department of US, General Hospital of Western Theater Command, Chengdu, China.
J Xray Sci Technol. 2022;30(6):1243-1260. doi: 10.3233/XST-221278.
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.
标准平面（SP）对于胎儿脑畸形的诊断至关重要。然而，由于胎儿姿势差异大，SP 定义复杂，准确获取 SP 非常耗时，且需要丰富的经验。
本研究旨在提出一种指导方法，帮助超声医师更准确、更快速地获取 SP。
首先，超声医师使用 3D 探头扫描胎儿头部以获取 3D 容积数据，然后使用仿射变换将 3D 容积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 探头扫描平面时，我们的 RLNet（回归定位网络）可以在 3D 头部模型中清晰显示当前平面的位置，从而帮助超声医师更准确地获取三个 SP。当定位到三个 SP 时，根据与三个 SP 的空间关系，自动生成矢状面和冠状面。
在 3200 张 2D US 图像上进行的实验结果表明，RLNet 获得的透明隔平面的平均角度误差为 3.91±2.86°，与已发表的数据相比有明显改善。自动生成的冠状面和矢状面 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，对未来的临床应用具有实用价值。
