  Ultrasound imaging is a standard examination during pregnancy that can be
used for measuring specific biometric parameters towards prenatal diagnosis and
estimating gestational age. Fetal head circumference (HC) is one of the
significant factors to determine the fetus growth and health. In this paper, a
multi-task deep convolutional neural network is proposed for automatic
segmentation and estimation of HC ellipse by minimizing a compound cost
function composed of segmentation dice score and MSE of ellipse parameters.
Experimental results on fetus ultrasound dataset in different trimesters of
pregnancy show that the segmentation results and the extracted HC match well
with the radiologist annotations. The obtained dice scores of the fetal head
segmentation and the accuracy of HC evaluations are comparable to the
state-of-the-art.
