WaveCSN

Abstract

Landmark detection in hip X-ray images plays a critical role in diagnosis of Developmental Dysplasia of the Hip (DDH) and surgeries of Total Hip Arthroplasty (THA). Regression and heatmap techniques of convolution network could obtain reasonable results. However, they have limitations in either robustness or precision given the complexities and intensity inhomogeneities of hip X-ray images. In this paper, we propose a Wave-like Cascade Segmentation Network (WaveCSN) to improve the accuracy of landmark detection by transforming landmark detection into area segmentation. The WaveCSN consists of three basic sub-networks and each sub-network is composed of a U-net module, an indicate module and a max-MSER module. The U-net undertakes the task to generate masks, and the indicate module is trained to distinguish the masks and ground truth. The U-net and indicate module are trained in turns, in which process the generated masks are supervised to be more and more alike to the ground truth. The max-MSER module ensures landmarks can be extracted from the generated masks precisely. We present two professional datasets (DDH and THA) for the first time and evaluate the WaveCSN on them. Our results prove that the WaveCSN can improve 2.66 and 4.11 pixels at least on these two datasets compared to other methods, and achieves the state-of-the-art for landmark detection in hip X-ray images.