Estimation of the Ankle-Joint Space Visibility in X-ray Images Using Convolutional Neural Networks

For the X-ray image acquisition one of the most important factors for diagnostic quality is the patient position with respect to the X-ray tube and the detector. In case of orthopedic lateral ankle examinations, inaccurate positioning might lead to a covered joint space. This could make a reliable reading of the images impossible, which necessitates a retake. The presented approach estimates the joint space visibility of lateral ankle X-ray images. An annotation method for the joint space visibility is proposed which depends on the condyle alignment of the talus. A Convolutional Neural Network (CNN) was trained to estimate the joint space visibility. Additionally, the plausibility of the approach was confirmed by an experimental phantom setup. The estimations on a clinical dataset show that using the quality measure in regression space results in a sensitivity of 0.85 and a specificity of 0.91 for a clinically reasonable definition of image quality.