Abstract
We present a novel automated approach for detection of standardized abdominal circumference (AC) planes in fetal ultrasound built in a convolutional neural network (CNN) framework, called SonoEyeNet, that utilizes eye movement data of a sonographer in automatic interpretation. Eye movement data was collected from experienced sonographers as they identified an AC plane in fetal ultrasound video clips. A visual heatmap was generated from the eye movements for each video frame. A CNN model was built using ultrasound frames and their corresponding visual heatmaps. Different methods of processing visual heatmaps and their fusion with image feature maps were investigated. We show that with the assistance of human visual fixation information, the precision, recall and F1-score of AC plane detection was increased to 96.5%, 99.0% and 97.8% respectively, compared to 73.6%, 74.1% and 73.8% without using eye fixation information.
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