Deep Learning-Based Methodology for Recognition of Fetal Brain Standard Scan Planes in 2D Ultrasound Images

Abstract

Two-dimensional ultrasound scanning (US) has become a highly recommended examination in prenatal diagnosis in many countries. Accurate detection of abnormalities and correct fetal brain standard planes is the most necessary precondition for successful diagnosis and measurement. In the past few years, support vector machine (SVM) and other machine learning methods have been devoted to automatic recognition of 2D ultrasonic images, but the performance of recognition is not satisfactory due to the wide diversity of fetal postures, shortage of data, similarities between standard planes and other reasons. Especially in the recognition of fetal brain images, the features of fetal brain images such as shape, texture, color and others are very similar, which presents great challenges to the recognition work. In this study, we proposed two main methods based on deep convolutional neural networks to automatically recognize six standard planes of fetal brains. One is a deep convolutional neural network (CNN), and the other one is CNN-based domain transfer learning. To examine the performance of these algorithms, we constructed two datasets. Dataset 1 consists of 30,000 2D ultrasound images from 155 subjects between 16 and 34 weeks. Dataset 2, containing 1,200 images, was acquired from a research participant throughout 40 weeks, which is the entire pregnancy. Experimental results show that the proposed solutions achieve promising results and that the frameworks based on deep convolutional neural networks generally outperform the ones using other classical deep learning methods, thus demonstrating the great potential of convolutional neural networks in this area.