Localization and measurement of fetal head in ultrasound image by deep neural networks

Ultrasound imaging is widely used in prenatal diagnosis of fetuses to monitor fetal development, which is of great clinical significance. The main purpose of prenatal diagnosis is to measure some biological parameters of fetuses, such as Abdominal Circumference (AC), Crown-rump Length (CRL), Femur Length (FL), Biparietal Diameter (BPD), Head Circumference (HC) and so on. However, the artifacts, noise and skull loss in fetal ultrasound images create some challenges to the automatic measurement of fetal HC. In order to improve the accuracy of automatic measurement of fetal HC, this paper proposes Feature Channel Spatial Unet(FCS-Unet) with post processing. Firstly, the fetal head positioning is carried out through Faster R-CNN to find out the coordinates of the fetal head positioning box. Then for fetal head circumference segmentation, Feature Pyramid Networks(FPN) is used to replace the encoder of Unet. And in the skip connections, channel and spatial module are incorporated through sum operation. In the post-processing section, the white pixels outside the head positioning box in the resulting binary images are zeroed and turned to black. Finally, HC is calculated by the least square ellipse fitting method which can obtain the ellipse parameters. Experimental results show that the proposed method is helpful to maintain the details of the fetal head, which has a good robustness to the low signal-to-noise ratio ultrasound images. To a certain extent, our method can improve the accuracy of automatic measurement of fetal HC.