Estimation of the ankle-joint space visibility in x-ray images using convolutional neural networks

Johannes Köpnick; Jan Marek May; Bernd Lundt; Matthias Brück; Christian Wülker

doi:10.1117/12.2651757

3 April 2023 Estimation of the ankle-joint space visibility in x-ray images using convolutional neural networks

Johannes Köpnick, Jan Marek May, Bernd Lundt, Matthias Brück, Christian Wülker

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246409 (2023) https://doi.org/10.1117/12.2651757
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Johannes Köpnick, Jan Marek May, Bernd Lundt, Matthias Brück, and Christian Wülker "Estimation of the ankle-joint space visibility in x-ray images using convolutional neural networks", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246409 (3 April 2023); https://doi.org/10.1117/12.2651757

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