Automated measurement of fetal head circumference using 2D ultrasound images

Abstract

In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, −0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.

Best technical abstract – Thomas LA van den Heuvel

“…The system for automated measurement of the HC using the high-end ultrasound device showed state of the art results on the largest dataset described in literature [4]. The study showed that automated measurement of the HC in 2 D images was feasible with an accuracy similar to the measurement made by a trained sonographer.…”

“…My research focused on the development of deep learning software that could automatically interpret the ultrasound data acquired using these sweeps. First, a sys-tem was developed for automated measurement of the fetal head circumference (HC), using 1334 acquired ultrasound images acquired with the high-end Voluson E8 and Voluson 730 (General Elecric, Austria) [4]. Secondly, a study was performed in Ethiopia, were the standardized acquisition protocol was acquired from 183 pregnant women using the mid-range SonoAce R3 (Samsung Medison, Korea) [5].…”

Confident Head Circumference Measurement from Ultrasound with Real-Time Feedback for Sonographers

“…Several images were taken during each session, including the standard transverse brain view at the posterior horn of the ventricle (TV) plane used for HC measurement. This data was combined with the HC18 Challenge [15] dataset which consists of 1334 two-dimensional US images of the standard plane that is used to measure HC, each image is 800x540 pixels with a pixel size ranging from 0.052mm to 0.326mm. Each image in the training set has an accompanying manual annotation of the HC (ellipse outline) performed by a single trained sonographer [15].…”

“…This data was combined with the HC18 Challenge [15] dataset which consists of 1334 two-dimensional US images of the standard plane that is used to measure HC, each image is 800x540 pixels with a pixel size ranging from 0.052mm to 0.326mm. Each image in the training set has an accompanying manual annotation of the HC (ellipse outline) performed by a single trained sonographer [15]. We resample all images to 320 × 384 pixels, and produce a head mask from the expert ground truth delineation.…”

Expected‐value bias: is it time to remove features from ultrasound machines?

“…Abele and colleagues, in an early study assessing SonoNT, concluded that using SonoNT reduced the variability in caliper placement between operators 5 . An increasing number of publications regarding different AI techniques for measurement of 2D fetal biometry based on supervised or unsupervised learning have been reported in recent years 6,7 . However, evaluation of the accuracy of biometric measurements using AI approaches is based on a comparison with that of sonographers.…”