IEEE J Biomed Health Inform. 2018 May;22(3):874-885. doi: 10.1109/JBHI.2017.2705031. Epub 2017 May 17.
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 × 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.
超声成像是产前诊断中一种常用的检查方法。准确获取胎儿面部标准平面（FFSP）是后续诊断和测量的最重要前提。在过去的几年中，已经使用各种手工制作的特征来进行 FFSP 识别，但是由于 FFSP 的类内变化较大，以及 FFSP 与其他非 FFSP 之间的高度视觉相似性，因此识别性能仍然不尽如人意。为了提高识别性能，我们提出了一种通过深度卷积神经网络（DCNN）架构自动识别 FFSP 的方法。所提出的 DCNN 由 16 个卷积层组成，卷积核较小，为 3×3，还有三个全连接层。在最后一个池化层中采用全局平均池化，以显著减少网络参数，从而减轻过拟合问题，并在有限的训练数据下提高性能。还实施了迁移学习策略和专门针对 FFSP 的数据增强技术，以进一步提高识别性能。广泛的实验证明了我们的方法相对于传统方法的优势，以及 DCNN 用于临床诊断的 FFSP 识别的有效性。
