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

Investigation of the role of feature selection and weighted voting in random forests for 3-D volumetric segmentation.

This paper describes a novel 3-D segmentation technique posed within the Random Forests (RF) classification framework. Two improvements over the traditional RF framework are considered. Motivated by the high redundancy of feature selection in the traditional RF framework, the first contribution develops methods to improve voxel classification by selecting relatively "strong" features and neglecting "weak" ones. The second contribution involves weighting each tree in the forest during the testing stage, to provide an unbiased and more accurate decision than provided by the traditional RF. To demonstrate the improvement achieved by these enhancements, experimental validation is performed on adult brain MRI and 3-D fetal femoral ultrasound datasets. In a comparison of the new method with a traditional Random Forest, the new method showed a notable improvement in segmentation accuracy. We also compared the new method with other state-of-the-art techniques to place it in context of the current 3-D medical image segmentation literature

Surface area measurement using rendered three-dimensional ultrasound imaging: an in-vitro phantom study.

OBJECTIVE: Cranial sutures and fontanelles can be reliably demonstrated using three-dimensional (3D) ultrasound with rendering. Our objective was to assess the repeatability and validity of fontanelle surface area measurement on rendered 3D images. METHODS: This was an in-vitro phantom validation study. Four holes, representing fontanelles, were cut on a flat vinyl tile. The phantom was scanned in a test-tank by two sonographers, at four different depths and using two different 3D sweep directions. The surface areas were measured on scan images and also directly from the phantom for comparison. Coefficients of variation (CVs), intraclass correlation coefficients (ICCs) and Bland-Altman plots were used for repeatability analysis. Validity was expressed as the percentage difference of the measured area from the true surface area. RESULTS: Validity of measurement was satisfactory with a mean percentage difference of - 5.9% (median = - 3.5%). The 95% limits of agreement were - 23.9 to 12.1%, suggesting that random error is introduced during image generation and measurement. Repeatability of caliper placement on the same image was higher (intraobserver CV = 1.6%, ICC = 0.999) than for measurement of a newly generated scan image (intraobserver CV = 5.5%, ICC = 0.992). Reduced accuracy was noted for the smallest shape tested. CONCLUSION: Surface area measurements on rendered 3D ultrasound images are accurate and reproducible in vitro

Fusion of 3D ultrasound images of the fetal femur improves boundary definition and volume measurement.

OBJECTIVE: To combine multiple 3D volumes of the same fetal femur into one composite image data set using image registration and wavelet-based fusion. Fused and single data sets were compared in terms of image quality and femur volume (FV) measurement repeatability. METHOD: In healthy pregnant volunteers, six volumes of the same femur were acquired and fused into a composite data set. Image quality scores were given to the fused and single data sets by an independent assessor in a blinded fashion; repeatability of FV measurement was assessed using coefficients of variation (CV), intraclass correlation coefficients (ICC) and Bland-Altman plots. RESULTS: Fusion was successful in 24 out of 25 cases. Median image quality score was 7/10 in fused data sets, compared to 6/10 in single data sets (p = 0.096). Repeatability of FV measurement was better in fused data sets (intraobserver CV 4.6% and ICC 0.987; interobserver CV 4.9%, ICC 0.985) compared to single ones (intraobserver CV 5.8%, ICC 0.977; interobserver CV 10.0%, ICC 0.931). The measured FV was significantly higher in fused data sets (mean FV 1.7 vs. 1.3 ml, p < 0.001). CONCLUSION: Image registration and wavelet-based fusion can improve image quality and FV repeatability; it also results in an increased FV measurement