Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
This addresses the need for efficient and accurate analysis of ultrasound videos in fetal heart screening, which is incremental as it builds on existing neural network methods with temporal enhancements.
The paper tackles the problem of automatically analyzing fetal cardiac screening videos by jointly predicting visibility, viewing plane, location, and orientation at the frame level, achieving performance comparable to expert annotations on a real-world clinical dataset.
We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart. Our method is able to jointly predict the visibility, viewing plane, location and orientation of the fetal heart at the frame level. The contributions of the paper are three-fold: (i) a convolutional neural network architecture is developed for a multi-task prediction, which is computed by sliding a 3x3 window spatially through convolutional maps. (ii) an anchor mechanism and Intersection over Union (IoU) loss are applied for improving localization accuracy. (iii) a recurrent architecture is designed to recursively compute regional convolutional features temporally over sequential frames, allowing each prediction to be conditioned on the whole video. This results in a spatial-temporal model that precisely describes detailed heart parameters in challenging US videos. We report results on a real-world clinical dataset, where our method achieves performance on par with expert annotations.