Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative

Abstract

Abstract Patients with developmental dysplasia of the hip can have this problem throughout their lifetime. The problem is difficult to detect by radiologists throughout x-ray because of an abrasion of anatomical structures. Thus, the landmarks should be automatically and precisely located. In this paper, we propose an attention mechanism of combining multi-dimension information on the basis of separating spatial dimension. The proposed attention mechanism decouples spatial dimension and forms width-channel dimension and height-channel dimension by 1D pooling operations in the height and width of spatial dimension. Then non-local means operations are performed to capture the correlation between long-range pixels in width-channel dimension, as well as that in height-channel dimension at different resolutions. The proposed attention mechanism modules are inserted into the skipped connections of U-Net to form a novel landmark detection structure. This landmark detection method was trained and evaluated through five-fold cross-validation on an open-source dataset, including 524 pelvis x-ray, each containing eight landmarks in pelvis, and achieved excellent performance compared to other landmark detection models. The average point-to-point errors of U-Net, HR-Net, CE-Net, and the proposed network were 3.5651 mm, 3.6118 mm, 3.3914 mm and 3.1350 mm, respectively. The results indicate that the proposed method has the highest detection accuracy. Furthermore, an open-source pelvis dataset is annotated and released for open research.