Skill Characterisation of Sonographer Gaze Patterns during Second Trimester Clinical Fetal Ultrasounds using Time Curves.

We present a method for skill characterisation of sonographer gaze patterns while performing routine second trimester fetal anatomy ultrasound scans. The position and scale of fetal anatomical planes during each scan differ because of fetal position, movements and sonographer skill. A standardised reference is required to compare recorded eye-tracking data for skill characterisation. We propose using an affine transformer network to localise the anatomy circumference in video frames, for normalisation of eye-tracking data. We use an event-based data visualisation, time curves, to characterise sonographer scanning patterns. We chose brain and heart anatomical planes because they vary in levels of gaze complexity. Our results show that when sonographers search for the same anatomical plane, even though the landmarks visited are similar, their time curves display different visual patterns. Brain planes also, on average, have more events or landmarks occurring than the heart, which highlights anatomy-specific differences in searching approaches.

End-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation.

This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.

Visualising Spatio-Temporal Gaze Characteristics for Exploratory Data Analysis in Clinical Fetal Ultrasound Scans.

Visualising patterns in clinicians' eye movements while interpreting fetal ultrasound imaging videos is challenging. Across and within videos, there are differences in size an d position of Areas-of-Interest (AOIs) due to fetal position, movement and sonographer skill. Currently, AOIs are manually labelled or identified using eye-tracker manufacturer specifications which are not study specific. We propose using unsupervised clustering to identify meaningful AOIs and bi-contour plots to visualise spatio-temporal gaze characteristics. We use Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to identify the AOIs, and use their corresponding images to capture granular changes within each AOI. Then we visualise transitions within and between AOIs as read by the sonographer. We compare our method to a standardised eye-tracking manufacturer algorithm. Our method captures granular changes in gaze characteristics which are otherwise not shown. Our method is suitable for exploratory data analysis of eye-tracking data involving multiple participants and AOIs.

Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation.

Purpose: We present an original method for simulating realistic fetal neurosonography images specifically generating third-trimester pregnancy ultrasound images from second-trimester images. Our method was developed using unpaired data, as pairwise data were not available. We also report original insights on the general appearance differences between second- and third-trimester fetal head transventricular (TV) plane images. Approach: We design a cycle-consistent adversarial network (Cycle-GAN) to simulate visually realistic third-trimester images from unpaired second- and third-trimester ultrasound images. Simulation realism is evaluated qualitatively by experienced sonographers who blindly graded real and simulated images. A quantitative evaluation is also performed whereby a validated deep-learning-based image recognition algorithm (ScanNav®) acts as the expert reference to allow hundreds of real and simulated images to be automatically analyzed and compared efficiently. Results: Qualitative evaluation shows that the human expert cannot tell the difference between real and simulated third-trimester scan images. 84.2% of the simulated third-trimester images could not be distinguished from the real third-trimester images. As a quantitative baseline, on 3000 images, the visibility drop of the choroid, CSP, and mid-line falx between real second- and real third-trimester scans was computed by ScanNav® and found to be 72.5%, 61.5%, and 67%, respectively. The visibility drop of the same structures between real second-trimester and simulated third-trimester was found to be 77.5%, 57.7%, and 56.2%, respectively. Therefore, the real and simulated third-trimester images were consider to be visually similar to each other. Our evaluation also shows that the third-trimester simulation of a conventional GAN is much easier to distinguish, and the visibility drop of the structures is smaller than our proposed method. Conclusions: The results confirm that it is possible to simulate realistic third-trimester images from second-trimester images using a modified Cycle-GAN, which may be useful for deep learning researchers with a restricted availability of third-trimester scans but with access to ample second trimester images. We also show convincing simulation improvements, both qualitatively and quantitatively, using the Cycle-GAN method compared with a conventional GAN. Finally, the use of a machine learning-based reference (in the case ScanNav®) for large-scale quantitative image analysis evaluation is also a first to our knowledge.

Cortical development in fetuses with congenital heart defects using an automated brain-age prediction algorithm.

INTRODUCTION: Congenital heart defects are associated with neurodevelopmental delay. It is hypothesized that fetuses affected by congenital heart defect have altered cerebral oxygen perfusion and are therefore prone to delay in cortical maturation. The aim of this study was to determine the difference in fetal brain age between consecutive congenital heart defect cases and controls in the second and third trimester using ultrasound. MATERIAL AND METHODS: Since 2014, we have included 90 isolated severe congenital heart defect cases in the Heart And Neurodevelopment (HAND)-study. Every 4 weeks, detailed neurosonography was performed in these fetuses, including the recording of a 3D volume of the fetal brain, from 20 weeks onwards. In all, 75 healthy fetuses underwent the same protocol to serve as a control group. The volumes were analyzed by automated age prediction software which determines gestational age by the assessment of cortical maturation. RESULTS: In total, 477 volumes were analyzed using the age prediction software (199 volumes of 90 congenital heart defect cases; 278 volumes of 75 controls). Of these, 16 (3.2%) volume recordings were excluded because of imaging quality. The age distribution was 19-33 weeks. Mixed model analysis showed that the age predicted by brain maturation was 3 days delayed compared with the control group (P = .002). CONCLUSIONS: This study shows that fetuses with isolated cases of congenital heart defects show some delay in cortical maturation as compared with healthy control cases. The clinical relevance of this small difference is debatable. This finding was consistent throughout pregnancy and did not progress during the third trimester.

Body composition at birth and its relationship with neonatal anthropometric ratios: the newborn body composition study of the INTERGROWTH-21st project.

BackgroundWe aimed to describe newborn body composition and identify which anthropometric ratio (weight/length; BMI; or ponderal index, PI) best predicts fat mass (FM) and fat-free mass (FFM).MethodsAir-displacement plethysmography (PEA POD) was used to estimate FM, FFM, and body fat percentage (BF%). Associations between FFM, FM, and BF% and weight/length, BMI, and PI were evaluated in 1,019 newborns using multivariate regression analysis. Charts for FM, FFM, and BF% were generated using a prescriptive subsample (n=247). Standards for the best-predicting anthropometric ratio were calculated utilizing the same population used for the INTERGROWTH-21st Newborn Size Standards (n=20,479).ResultsFFM and FM increased consistently during late pregnancy. Differential FM, BF%, and FFM patterns were observed for those born preterm (34+0-36+6 weeks' gestation) and with impaired intrauterine growth. Weight/length by gestational age (GA) was a better predictor of FFM and FM (adjusted R2=0.92 and 0.71, respectively) than BMI or PI, independent of sex, GA, and timing of measurement. Results were almost identical when only preterm newborns were studied. We present sex-specific centiles for weight/length ratio for GA.ConclusionsWeight/length best predicts newborn FFM and FM. There are differential FM, FFM, and BF% patterns by sex, GA, and size at birth.

International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project.

BACKGROUND: In 2006, WHO published international growth standards for children younger than 5 years, which are now accepted worldwide. In the INTERGROWTH-21(st) Project, our aim was to complement them by developing international standards for fetuses, newborn infants, and the postnatal growth period of preterm infants. METHODS: INTERGROWTH-21(st) is a population-based project that assessed fetal growth and newborn size in eight geographically defined urban populations. These groups were selected because most of the health and nutrition needs of mothers were met, adequate antenatal care was provided, and there were no major environmental constraints on growth. As part of the Newborn Cross-Sectional Study (NCSS), a component of INTERGROWTH-21(st) Project, we measured weight, length, and head circumference in all newborn infants, in addition to collecting data prospectively for pregnancy and the perinatal period. To construct the newborn standards, we selected all pregnancies in women meeting (in addition to the underlying population characteristics) strict individual eligibility criteria for a population at low risk of impaired fetal growth (labelled the NCSS prescriptive subpopulation). Women had a reliable ultrasound estimate of gestational age using crown-rump length before 14 weeks of gestation or biparietal diameter if antenatal care started between 14 weeks and 24 weeks or less of gestation. Newborn anthropometric measures were obtained within 12 h of birth by identically trained anthropometric teams using the same equipment at all sites. Fractional polynomials assuming a skewed t distribution were used to estimate the fitted centiles. FINDINGS: We identified 20,486 (35%) eligible women from the 59,137 pregnant women enrolled in NCSS between May 14, 2009, and Aug 2, 2013. We calculated sex-specific observed and smoothed centiles for weight, length, and head circumference for gestational age at birth. The observed and smoothed centiles were almost identical. We present the 3rd, 10th, 50th, 90th, and 97th centile curves according to gestational age and sex. INTERPRETATION: We have developed, for routine clinical practice, international anthropometric standards to assess newborn size that are intended to complement the WHO Child Growth Standards and allow comparisons across multiethnic populations. FUNDING: Bill & Melinda Gates Foundation.