Evaluation of fetal head circumference (hc) and biparietal diameter (bpd (biparietal diameter)) in ultrasound images using multi-task deep convolutional neural network

Abstract

Ultrasound imaging is a standard examination during pregnancy that can measure specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is a significant factor in determining fetus growth and health. This paper proposes a multi-task deep convolutional neural network for automatic segmentation and estimation of HC (Fetal head circumference) ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD (BIPARIETAL DIAMETER)), are commonly used to evaluate the gestational age and diagnose fetal central nervous system (CNS) pathology. Since manual measurements are operator-dependent and time-consuming, there have been numerous researches on automated methods. However, existing computerized methods still are not satisfactory in terms of accuracy and reliability, owing to difficulties in dealing with various artefacts in ultrasound images. This paper focuses on fetal head biometry and develops a deep-learning-based method for estimating HC (Fetal head circumference) and BPD (BIPARIETAL DIAMETER) with a high degree of accuracy and reliability. The proposed method effectively identifies the head boundary by differentiating tissue image patterns concerning the ultrasound propagation direction. The proposed method was trained with 102 labelled data set and tested to 70 ultrasound images. We achieved a success rate of 92.31% for HC (Fetal head circumference) and BPD (BIPARIETAL DIAMETER) estimations and an accuracy of 87.14% for the plane acceptance check.