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One of the routine examinations that are used for prenatal care in manycountries is ultrasound imaging. This procedure provides various informationabout fetus health and development, the progress of the pregnancy and, thebaby's due date. Some of the biometric parameters of the fetus, like fetal headcircumference (HC), must be measured to check the fetus's health and growth. Inthis paper, we investigated the effects of using multi-scale inputs in thenetwork. We also propose a light convolutional neural network for automatic HCmeasurement. Experimental results on an ultrasound dataset of the fetus indifferent trimesters of pregnancy show that the segmentation accuracy and HCevaluations performed by a light convolutional neural network are comparable todeep convolutional neural networks. The proposed network has fewer parametersand requires less training time.

Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks