Ellipse guided multi-task network for fetal head circumference measurement

Precise fetal head circumference measurement by ultrasound imaging is of great significance for prenatal examination. However, missing or blurring boundaries caused by artifacts and noises challenge measurement accuracy. The inconsistency between the segmentation pseudo-label and the ellipse contours also generates measurement errors. To improve the measurement performances of fetal head circumference, in this study, we propose an ellipse-guided multi-task network that measures the fetal head circumference according to detected ellipse boundary pixels. In the proposed network, an region segmentation branch is designed to learn region features of the fetal head, and a feature fusion module is applied to combine region features with boundary features, which contribute to exploring more context information about the fetal head and locating boundary pixels in boundary missing or blurring regions. A loss function is also designed in the network to ensure the boundary estimation in an ellipse shape. Experiments are conducted on both the public fetal head circumference measurement dataset HC-18 and a self-built ultrasonic phantom dataset. The experimental results show that the proposed method achieves DSC 97.97%, HD 1.22 mm, and ADF 1.85 mm, which demonstrates that the proposed method achieves excellent performance to compete with other state-of-the-art methods in fetal head circumference measurement.