A Kernel Density Estimation based Quality Metric for Quality Assessment of Obstetric Ultrasound Video

Jong Kwon, Jianbo Jiao, Alice Self, Alison Noble, Aris Papageorghiou

Keywords: Quality, Ultrasound, Deep-learning, Quality-assessment, Detection, Kernel-density-estimation

Abstract: Simplified ultrasound scanning protocols (sweeps) have been developed to reduce the high skill required to perform a regular obstetric ultrasound examination. However, without automated quality assessment of the video, the utility of such protocols in clinical practice is limited. An automated quality assessment algorithm is proposed that applies an object detector to detect fetal anatomies within ultrasound videos. Kernel density estimation is applied to the bounding box annotations to estimate a probability density function of certain bounding box properties such as the spatial and temporal position during the sweeps. This allows quantifying how well the spatio-temporal position of anatomies in a sweep agrees with previously seen data as a quality metric. The new quality metric is compared to other metrics of quality such as the confidence of the object detector model.