Statistical Characterisation of Fetal Anatomy in Simple Obstetric Ultrasound Video Sweeps

Abstract

Background: We present a statistical characterisation of fetal anatomies in obstetric ultrasound video sweeps where the transducer follows a fixed trajectory on the maternal abdomen. Methods: Large-scale, frame-level manual annotations of fetal anatomies (head, spine, abdomen, pelvis, femur) are used to compute common frame-level anatomy detection patterns expected for breech, cephalic, and transverse fetal presentations, with respect to video sweep paths. The patterns, termed statistical heatmaps, quantify the expected anatomies seen in a simple obstetric ultrasound video sweep protocol. In this study, we use a total of 760 unique manual annotations from 365 unique pregnancies. Results: We provide a qualitative interpretation of the heatmaps assessing the transducer sweep paths with respect to different fetal presentations and suggest ways the heatmaps can be applied in computational research (e.g. as a machine learning prior). Conclusions: The heatmap parameters are freely available to other researchers (https://www.github.com/agleed/calopus_statistical_heatmaps).