Two-Dimensional Echocardiography Estimates of Fetal Ventricular Mass throughout Gestation.

BACKGROUND: Two-dimensional (2D) ultrasound quality has improved in recent years. Quantification of cardiac dimensions is important to screen and monitor certain fetal conditions. We assessed the feasibility and reproducibility of fetal ventricular measures using 2D echocardiography, reported normal ranges in our cohort, and compared estimates to other modalities. METHODS: Mass and end-diastolic volume were estimated by manual contouring in the four-chamber view using TomTec Image Arena 4.6 in end diastole. Nomograms were created from smoothed centiles of measures, constructed using fractional polynomials after log transformation. The results were compared to those of previous studies using other modalities. RESULTS: A total of 294 scans from 146 fetuses from 15+0 to 41+6 weeks of gestation were included. Seven percent of scans were unanalysable and intraobserver variability was good (intraclass correlation coefficients for left and right ventricular mass 0.97 [0.87-0.99] and 0.99 [0.95-1.0], respectively). Mass and volume increased exponentially, showing good agreement with 3D mass estimates up to 28 weeks of gestation, after which our measurements were in better agreement with neonatal cardiac magnetic resonance imaging. There was good agreement with 4D volume estimates for the left ventricle. CONCLUSION: Current state-of-the-art 2D echocardiography platforms provide accurate, feasible, and reproducible fetal ventricular measures across gestation, and in certain circumstances may be the modality of choice

International standards for fetal brain structures based on serial ultrasound measurements from the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project.

OBJECTIVE: To create prescriptive growth standards for five fetal brain structures, measured by ultrasound, from healthy, well-nourished women, at low risk of impaired fetal growth and poor perinatal outcomes, taking part in the Fetal Growth Longitudinal Study (FGLS) of the INTERGROWTH-21st Project. METHODS: This was a complementary analysis of a large, population-based, multicentre, longitudinal study. We measured, in planes reconstructed from 3-dimensional (3D) ultrasound volumes of the fetal head at different time points in pregnancy, the size of the parieto-occipital fissure (POF), Sylvian fissure (SF), anterior horn of the lateral ventricle (AV), atrium of the posterior ventricle (PV) and cisterna magna (CM). The sample analysed was randomly selected from the overall FGLS population, ensuring an equal distribution amongst the eight diverse participating sites and of 3D ultrasound volumes across pregnancy (range: 15 - 36 weeks' gestation). Fractional polynomials were used to the construct standards. Growth and development of the infants were assessed at 1 and 2 years of age to confirm their adequacy for constructing international standards. RESULTS: From the entire FGLS cohort of 4321 women, 451 (10.4%) were randomly selected. After exclusions, 3D ultrasound volumes from 442 fetuses born without congenital malformations were used to create the charts. The fetal brain structures of interest were identified in 90% of cases. All structures showed increasing size with gestation and increasing variability for the POF, SF, PV and CM. The 3rd , 5th , 50th , 95th and 97th smoothed centile are presented. The 5th centile of POF and SF were 2.8 and 4.3 at 22 weeks and 4.2 and 9.4mm at 32 weeks respectively. The 95th centile of PV and CM were 8.5 and 7.4 at 22 weeks and 8.5 and 9.4mm at 32 weeks respectively. CONCLUSIONS: We have produced prescriptive size standards for fetal brain structures based on prospectively enrolled pregnancies at low risk of abnormal outcomes. We recommend these as international standards for the assessment of measurements obtained by ultrasound from fetal brain structures

Should we use the “ellipse” or “two diameters” method to measure fetal head and abdominal circumferences?

Objectives: To assess the reproducibility, throughout gestation, of the two most commonly used methods to measure fetal head (HC) and abdominal (AC) circumferences, namely fitting of an “ellipse” and measuring “two diameters”. Methods: Women participating in the INTERGROWTH-21st Project had serial ultrasound scans every 5 ± 1 weeks from 9-14 weeks of gestation until delivery. At each visit, HC and AC were measured in triplicate in two ways: using the ellipse facility (HCellipse and ACellipse) and by measuring two perpendicular diameters (biparietal and occipitofrontal diameter of the head, and transverse and antero-posterior diameters of the abdomen), to calculate HCcalc and ACcalc, respectively. To assess the intra-observer reproducibility, the standard deviation (SD) for each of the triplicate measurements was compared between the two methods (paired t-test). Inter-observer reproducibility of caliper replacement was performed on a random sub-sample of 10% of the scans. Results: A total of 20,029 scans provided measurements using both methods. The intra-observer variability based on the triplicate measurements for the two methods was not significantly different (HCellipse vs HCcalc p = 0.41; ACellipse vs ACcalc p = 0.36). The mean systematic differences (95% limits of agreement) for the inter-observer variability were all similar (HCellipse: −2.4 (6.6, −11.5); HCcalc: −1.9 (7.7, −11.6); ACellipse: 0.10 (11.6, −11.4); ACcalc: 0.06 (11.8, −12.0) mm). Conclusions: There are minimal differences in measurement variability between the ellipse and two diameters methods, which suggests the two methods are equally reproducible. There appears to be no advantage in using one method over the other in clinical practice

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

© 2017 IEEE. Placental volume measured with 3D ultrasound in the first trimester has been shown to be correlated to adverse pregnancy outcomes. This could potentially be used as a screening test to predict the 'at risk' pregnancy. However, manual segmentation whilst previously shown to be accurate and repeatable is very time consuming and semi-automated methods still require operator input. To generate a screening tool, fully automated placental segmentation is required. In this work, a deep convolutional neural network (cNN), DeepMedic, was trained using the output of the semi-automated Random Walker method as ground truth. 300 3D ultrasound scans of first trimester placentas were used to train, validate and test the cNN. Compared against the semi-automated segmentation, resultant median (1 st Quartile, 3 rd Quartile) Dice Similarity Coefficient was 0.73 (0.66, 0.76). The median (1 st Quartile, 3 rd Quartile) Hausdorff distance was 27 mm (18 mm, 36 mm). We present the first attempt at using a deep cNN for segmentation of 3D ultrasound of the placenta. This work shows that feasible results compared to ground truth were obtained that could form the basis of a fully automatic segmentation method

Fully automated, real-time 3D-ultrasound segmentation to estimate first trimester placental volume using deep learning

Objectives: We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator-dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. Methods: The placenta was segmented from 2393 first trimester 3D-US volumes using a semi-automated technique. This was quality controlled by three operators to produce the ‘ground-truth’ dataset. A fully convolutional neural network (OxNNet) was trained using this ‘ground-truth’ dataset to automatically segment the placenta. Findings: OxNNet delivered state of the art automatic segmentation (median Dice similarity coefficient of 0.84). The effect of training set size on the performance of OxNNet demonstrated the need for large datasets (n=1200, median DSC (inter-quartile range) 0.81 (0.15)). The clinical utility of placental volume was tested by looking at prediction of small-for-gestational-age (SGA) babies at term. The receiver-operating characteristics curves demonstrated almost identical results (OxNNet 0.65 (95% CI; 0.61-0.69) and ‘ground-truth’ 0.65 (95% CI; 0.61-0.69)). Conclusions: Our results demonstrated good similarity to the ‘ground-truth’ and almost identical clinical results for the prediction of SGA. Our open source software, OxNNet, and trained models are available on request.</p

Fully automated 3D ultrasound segmentation of the placenta, amniotic fluid and fetus for early pregnancy assessment

Volumetric placental measurement using 3D ultrasound has proven clinical utility in predicting adverse pregnancy outcomes. However, this metric can not currently be employed as part of a screening test due to a lack of robust and real-time segmentation tools. We present a multi-class convolutional neural network (CNN) developed to segment the placenta, amniotic fluid and fetus. The ground truth dataset consisted of 2,093 labelled placental volumes augmented by 300 volumes with placenta, amniotic fluid and fetus annotated. A two pathway, hybrid model using transfer learning, a modified loss function and exponential average weighting was developed and demonstrated the best performance for placental segmentation, achieving a Dice similarity coefficient (DSC) of 0.84 and 0.38 mm average Hausdorff distance (HDAV). Use of a dual-pathway architecture, improved placental segmentation by 0.03 DSC and reduced HDAV by 0.27mm when compared with a naïve multi-class model. Incorporation of exponential weighting produced a further small improvement in DSC by 0.01 and a reduction of HDAV by 0.44mm. Per volume inference using the FCNN took 7-8 seconds. This method should enable clinically relevant morphometric measurements (such as volume and total surface area) to be automatically generated for the placenta, amniotic fluid and fetus. Ready availability of such metrics makes a population-based screening test for adverse pregnancy outcomes possible

Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning

Placental volume measured with 3D ultrasound in the first trimester has been shown to be correlated to adverse pregnancy outcomes. This could potentially be used as a screening test to predict the 'at risk' pregnancy. However, manual segmentation whilst previously shown to be accurate and repeatable is very time consuming and semi-automated methods still require operator input. To generate a screening tool, fully automated placental segmentation is required. In this work, a deep convolutional neural network (cNN), DeepMedic, was trained using the output of the semi-automated Random Walker method as ground truth. 300 3D ultrasound scans of first trimester placentas were used to train, validate and test the cNN. Compared against the semi-automated segmentation, resultant median (1 st Quartile, 3 rd Quartile) Dice Similarity Coefficient was 0.73 (0.66, 0.76). The median (1 st Quartile, 3 rd Quartile) Hausdorff distance was 27 mm (18 mm, 36 mm). We present the first attempt at using a deep cNN for segmentation of 3D ultrasound of the placenta. This work shows that feasible results compared to ground truth were obtained that could form the basis of a fully automatic segmentation method

Fully automated, real-time 3D-ultrasound segmentation to estimate first trimester placental volume using deep learning

We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA