Texture analysis of sonographic image of placenta in pregnancies with normal and adverse outcomes, a pilot study.

INTRODUCTION: Many maternal and fetal morbidity and mortality from complications of pregnancy have been attributed to placenta abnormality. Placenta assessment in developing countries is mainly through ultrasonography which is subjective and prone to error. Objective assessment of placental abnormalities through texture analysis has been frequently done using magnetic resonance images with little done on ultrasound generated images, thus, the need for this study. The study is aimed at using statistical texture analysis in characterizing placenta tissue into normal and abnormal placenta as well as testing the accuracy of different texture analysis algorithms in differentiating placenta into normal and abnormal placental tissues. METHODS: This longitudinal study involved 500 ultrasound-generated placenta images from patients screened for adverse pregnancy outcomes in a private hospital in Enugu. These images were loaded onto an HP laptop for viewing. Two regions of interest were selected from the placenta tissue where texture features were extracted and were classified into normal and abnormal placentas using MaZda® software version 47 while the accuracy of the classification descriptors was assessed using WEKA classification algorithms. RESULTS: Co-occurrence matrix, run length matrix and histogram parameters differentiated normal placenta tissue from abnormal placental tissues (p-value <0.05) while variance is the only absolute gradient parameter that can differentiate normal placenta tissue from abnormal placenta tissues. All feature descriptors show high classification accuracy using KNN and ANN algorithms. CONCLUSION: Texture analysis can differentiate normal placenta tissues from abnormal placenta tissue which will reduce the errors associated with subjective assessment of the placenta echogenicity. IMPLICATIONS FOR PRACTICE: Integrating these computer-aided algorithms into our ultrasound machines will lead to early detection of abnormal placenta tissues as early management results in better pregnancy outcomes.

Sonographic correlation of foetal neck circumference and area with gestational age among pregnant women in Port Harcourt, Nigeria.

The purpose of this study was to create a reference range nomogram of foetal neck circumference (FNC) and foetal neck area (FNA) in a Nigerian population using polynomial regression models. This cross-sectional study involved 723 pregnant women between 14 and 40 weeks of gestation. Axial measurements of the FNC and FNA were obtained in three measurements and the mean taken as the final value and the 5th, 50th and 95th percentiles for each foetal gestational age (FGA) were calculated. FNC and FNA correlated strongly with FGA, biparietal diameter, abdominal circumference, head circumference, and femoral length. Cubic models fitted the FNC vs FGA, and FNA vs. FGA values, and the mathematical relationships are given as: [Formula: see text] [Formula: see text] [Formula: see text]. Nomograms of FNC and FNA are thus generated. Impact statement The foetal neck circumference (FNC) and foetal neck area (FNA) can serve as predictors of foetal gestational age (FGA) since they correlate strongly and positively with FGA and known biometric parameters. The measurements obtained vary with the population studied. This study provides a nomogram of the FNA and FNC for an African population. The values correlate with that of the Caucasian population up to 32 weeks FGA. Interestingly, FNA and FNC measurements demonstrate high correlation but poor agreement in measurements between sonographers. Even though FNA and FNC could be used as predictors of foetal gestational age, the measurements vary significantly between sonographers. This is attributable to the difficulty in obtaining a satisfactory axial view of foetal neck, which is dependent on foetal presentation.