Tsai Pei-Yin, Hung Ching-Hui, Chen Chi-Yeh, Sun Yung-Nien
Department of Obstetrics and Gynecology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70104, Taiwan.
Institute of Computer Science and Information Engineering & Institute of Medical Informatics, National Cheng Kung University, Tainan 70104, Taiwan.
Diagnostics (Basel). 2020 Dec 24;11(1):21. doi: 10.3390/diagnostics11010021.
In the first trimester of pregnancy, fetal growth, and abnormalities can be assessed using the exact middle sagittal plane (MSP) of the fetus. However, the ultrasound (US) image quality and operator experience affect the accuracy. We present an automatic system that enables precise fetal MSP detection from three-dimensional (3D) US and provides an evaluation of its performance using a generative adversarial network (GAN) framework.
The neural network is designed as a filter and generates masks to obtain the MSP, learning the features and MSP location in 3D space. Using the proposed image analysis system, a seed point was obtained from 218 first-trimester fetal 3D US volumes using deep learning and the MSP was automatically extracted.
The experimental results reveal the feasibility and excellent performance of the proposed approach between the automatically and manually detected MSPs. There was no significant difference between the semi-automatic and automatic systems. Further, the inference time in the automatic system was up to two times faster than the semi-automatic approach.
The proposed system offers precise fetal MSP measurements. Therefore, this automatic fetal MSP detection and measurement approach is anticipated to be useful clinically. The proposed system can also be applied to other relevant clinical fields in the future.
在妊娠早期，可以使用胎儿的正中矢状平面（MSP）来评估胎儿生长及有无异常情况。然而，超声（US）图像质量和操作者经验会影响准确性。我们提出一种自动系统，该系统能够从三维（3D）超声中精确检测胎儿MSP，并使用生成对抗网络（GAN）框架对其性能进行评估。
将神经网络设计为一个滤波器，生成掩码以获取MSP，从而学习三维空间中的特征和MSP位置。使用所提出的图像分析系统，通过深度学习从218例妊娠早期胎儿的3D超声容积中获取一个种子点，并自动提取MSP。
实验结果表明了所提出方法在自动检测和手动检测的MSP之间的可行性和优异性能。半自动系统和自动系统之间没有显著差异。此外，自动系统的推理时间比半自动方法快两倍。
所提出的系统能够提供精确的胎儿MSP测量值。因此，这种自动胎儿MSP检测和测量方法预计在临床上会很有用。所提出的系统未来也可应用于其他相关临床领域。
