The ultrasound detection of the fetus is of great significante to judge or predict the pregnancy and estimating the fetal size and quality. Ultrasonic measurement is mainly conducted in abdominal circumference, femur length and crown-rump length of the fetus. Measurement of these biological parameters can be used to determine whether the fetal growth is good or deformed. In general, these parameters need to be measured by doctor manually, which is inefficient because of repeated operations and often misdiagnosed due to shortcomings such as relying on the doctor's experience. In response to this kind of problem, in this paper, we propose a deep learning-based method to automatically measure the abdominal circumference, femur length and crown-rump length. The method is based on the UNET structure and combines the strategy of dilation convolution to improve the network's ability to extract context information and named it D-UNET. Validation was done on three datasets: abdominal circumference, femur and crown-rump length and compared with some other advanced deep learning segmentation algorithms. Clinically, our method demonstrated measurements that were very close to the manual labeling by expert physicians on all three datasets. This shows that our method can assist physicians in the automatic measurement of these biological parameters.
