CNN-based pose estimation for assessing quality of ankle-joint X-ray images

A novel technology for estimating both the pose and the joint flexion from a single musculoskeletal X-ray image is presented for automatic quality assessment of patient positioning. The method is based on convolutional neural networks and does not require pose or flexion labels of the X-ray images for the training phase. The task is split into two steps: (i) detection of relevant bone contours in the X-ray by a feature-detection network and (ii) regression of the pose and flexion parameters by a pose-estimation network based upon the detected contours. This separation enables the pose-estimation network to be trained using synthetic contours, which are generated via projections of an articulated 3D model of the target anatomy. It is demonstrated that the use of data-augmentation techniques during training of the pose-estimation network significantly contributes to the robustness of the algorithm. Feasibility of the approach is illustrated using lateral ankle X-ray exams. Validation was performed using X-rays of an anthropomorphic phantom of the foot-ankle joint, imaged in various controlled positions. Reference pose parameters were established by an expert using an interactive tool to align the articulated 3D joint model with the phantom image. Errors in pose estimation are in the range of 2 degrees per pose angle and at the level of the expert performance. Using the rigid foot phantom the flexion parameter was constant, but the overall results indicate accurate estimation also of this parameter.