Abstract

We present a novel multi-task neural network called Temporal SonoEyeNet (TSEN) with a primary task to describe the visual navigation process of sonographers by learning to generate visual attention maps of ultrasound images around standard biometry planes of the fetal abdomen, head (trans-ventricular plane) and femur. TSEN has three components: a feature extractor, a temporal attention module (TAM), and an auxiliary video classification module (VCM). A soft dynamic time warping (sDTW) loss function is used to improve visual attention modelling. Variants of the model are trained on a dataset of 280 video clips, each containing one of the three biometry planes and lasting 3-7 seconds, with corresponding real-time recorded gaze tracking data of an experienced sonographer. We report the performances of the different variants of TSEN for visual attention prediction at standard biometry plane detection. The best model performance is achieved using bi-directional convolutional long-short term memory (biCLSTM) in both TAM and VCM, and it outperforms a previous spatial model on all static and dynamic saliency metrics. As an auxiliary task to validate the clinical relevance of the visual attention modelling, the predicted visual attention maps were used to guide standard biometry plane detection in consecutive US video frames. All spatio-temporal TSEN models achieve higher scores compared to a spatial-only baseline; the best performing TSEN model achieves F1 scores on these standard biometry planes of 83.7%, 89.9% and 81.1%, respectively.