Automatical Localization of X-ray Cephalometric Images Landmarks via U-type Network

In order to improve the efficiency of oral clinical diagnosis, this paper proposes an automatic location method for structural feature points of X-ray cephalometric images based on U-type generative network. This method constructs a U-type generator network under the framework of a generative adversarial network to learn the mapping from the current image to the target landmark offset distance map; then builds a discriminator network to determine whether the predicted offset distance map is consistent with the real data. That is to say, in this paper, image is the output of the U-type generating adversarial network, rather than the common output displacement or coordinate value. The newly acquired X-ray cephalometric image is used as the input of the trained U-type generator network to obtain the offset distance map of the new image to the target feature point, and then the detection is obtained from the predicted offset distance map through the regression voting method Target feature point coordinates. The experimental results show that the method in this paper gets higher detection rate than others,and it can accurately obtain the position of the structural feature points in the X-ray cephalometric image.