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Ultrasound imaging is a standard examination during pregnancy that can beused for measuring specific biometric parameters towards prenatal diagnosis andestimating gestational age. Fetal head circumference (HC) is one of thesignificant factors to determine the fetus growth and health. In this paper, amulti-task deep convolutional neural network is proposed for automaticsegmentation and estimation of HC ellipse by minimizing a compound costfunction composed of segmentation dice score and MSE of ellipse parameters.Experimental results on fetus ultrasound dataset in different trimesters ofpregnancy show that the segmentation results and the extracted HC match wellwith the radiologist annotations. The obtained dice scores of the fetal headsegmentation and the accuracy of HC evaluations are comparable to thestate-of-the-art.

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning