Intelligent Recognition of the Ultrasound Standard Plane of the Fetal Cranial Brain with the FCB-Net

Abstract

Ultrasound images can be acquired in real-time and quickly, and also have the advantage of being low cost and no radiation. Currently, ultrasound is widely used in clinical diagnosis. With the development of ultrasound, it is slowly becoming an essential part of the imaging examinations in obstetrics. Fetal cranial ultrasound plays a vital role in assessing fetal growth and development, decreasing the rate of birth defects, monitoring pregnancy, and assessing clinical diagnosis. Due to its ability to visualize the internal structures of the fetal cranial brain in standard planes, fetal cranial ultrasound is also essential in screening for fetal structural abnormalities. However, the conventional approach relies primarily on the ultrasound doctor to do the work manually, which is a time-consuming and laborious process. This paper proposed a convolutional neural network, FCB-Net, for recognition of the ultrasound standard plane of the fetal cranial brain. There are 5361 fetal intracranial ultrasound images collected, and they were randomly divided into 4258 for model training and 1103 for testing the performance of the model. The experiments have shown that our proposed FCB-Net has the best recognition performance for the fetal cranial brain, and the accuracy of FCB-Net has reached 91.66%.