First Trimester Video Saliency Prediction Using Clstmu-Net With Stochastic Augmentation

In this paper we develop a multi-modal video analysis algorithm to predict where a sonographer should look next. Our approach uses video and expert knowledge, defined by gaze tracking data, which is acquired during routine first-trimester fetal ultrasound scanning. Specifically, we propose a spatio-temporal convolutional LSTMU-Net neural network (cLSTMU-Net) for video saliency prediction with stochastic augmentation. The architecture design consists of a U-Net based encoder-decoder network and a cLSTM to take into account temporal information. We compare the performance of the cLSTMU-Net alongside spatial-only architectures for the task of predicting gaze in first trimester ultrasound videos. Our study dataset consists of 115 clinically acquired first trimester US videos and a total of 45,666 video frames. We adopt a Random Augmentation strategy (RA) from a stochastic augmentation policy search to improve model performance and reduce over-fitting. The proposed cLSTMU-Net using a video clip of 6 frames outperforms the baseline approach on all saliency metrics: KLD, SIM, NSS and CC (2.08, 0.28, 4.53 and 0.42 versus 2.16, 0.27, 4.34 and 0.39).