Orientation of the hip cup is important in total hip arthroplasties. Orientation includes abduction (inclination) and anteversion. Anteversion can be considered as true (anatomic) and planar (radiographic) anteversion. Some measurement methods either are too complicated or are less precise. We developed a new protractor to measure cup orientation using postoperative anteroposterior radiographs centered at the hip. The new protractor measures true and planar anteversion and abduction easily and precisely. We verified its accuracy using a software simulator and simulated 45 radio- graphs of total hip arthroplasties with 15 different anteversions ranging from 15 degrees -29 degrees and 45 actual radiographs of total hip arthroplasties. We then measured the planar ante- version with our method and the method of Lewinnek et al. Maximal errors were 3 degrees and 2.61 degrees , respectively, and mean errors were 0.96 degrees and 1.2 degrees , respectively. The standard deviations were 0.74 degrees with our method and 0.57 degrees with the method of Lewinnek et al. For the real radiographs, the mean of absolute difference between the two methods was 1.34 degrees , and the standard deviation was 1.13 degrees . We found no difference between the two methods and no difference in our findings compared with those of Pradhan.

Depth estimation features are helpful for 3D recognition. Commodity-grade depth cameras are able to capture depth and color image in real-time. However, glossy, transparent or distant surface cannot be scanned properly by the sensor. As a result, enhancement and restoration from sensing depth is an important task. Depth completion aims at filling the holes that sensors fail to detect, which is still a complex task for machine to learn. Traditional hand-tuned methods have reached their limits, while neural network based methods tend to copy and interpolate the output from surrounding depth values. This leads to blurred boundaries, and structures of the depth map are lost. Consequently, our main work is to design an end-to-end network improving completion depth maps while maintaining edge clarity. We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced. In addition, we propose boundary consistency concept to enhance the depth map quality and structure. Experimental results validate the effectiveness of our selfattention and boundary consistency schema, which outperforms previous state-of-the-art depth completion work on Matterport3D dataset. Our code is publicly available at

No abstract