Cephalometric landmark detection by considering translational invariance in the two-stage framework

Recently, intelligent cephalometric landmark detection systems that employ deep convolutional neural networks (DCNNs) have been widely developed. In this paper, we concentrate on two challenging difficulties of DCNNs based cephalometric landmark detection. First, previous DCNN methods usually decreased the resolution of high-resolution cephalometric images. Hence, the trained DCNNs missed the detailed local features of original images. Moreover, the detected landmarks’ locations were recovered using a fixed ratio, so location error was increased after recovery. Second, the performance of cephalometric landmark detection is dependent on DCNNs’ translational invariance because the target landmarks’ locations are influenced by any shift of the cephalometric images. However, modern DCNNs have limited translational invariance, and previous DCNN methods did not take translational invariance into consideration. In this paper, we developed the widely used two-stage framework to resolve the above two challenges. In the first stage, we train a global detection network, which receives the whole images as input, to generate candidate landmarks. In the second stage, we split the input image into patches and train a local refine network, which receives image patches as input, to refine the landmarks’ locations. The proposed local refine network can produce accurate landmarks’ locations because it entirely focuses on the detailed local features of high-resolution cephalometric images. The global detection network and local refine network are structured with translational invariance as their primary consideration. The proposed method achieved robust performance on a private dataset and the public Automatic Cephalometric X-ray Landmark Detection Challenge 2015 dataset.