Automated characteriazation of the fetal heart in ultrasound images using fully convolutional neural networks - Sundaresan, V; Bridge, CP; Ioannou, C; Noble, JA (IEEE, 2017)
Automatic analysis of fetal echocardiography screening images could aid in the identification of congenital heart diseases. The first step towards automatic fetal echocardiography analysis is locating the fetal heart in an image and identifying the viewing (imaging) plane. This is highly challenging since the fetal heart is small with relatively indistinct anatomical structural appearance. This is further compounded by the presence of artefacts in ultrasound images. Herein we provide a state-of-art solution for detecting the fetal heart and classifying each individual frame as belonging to one of the standard viewing planes using fully convolutional neural networks (FCNs). Our FCN model achieves a classification error rate of 23.48% on real-world clinical ultrasound data. We also present comparative performance for analysis of different FCN architectures.