Patient motion is one of the major challenges in cone-beam computed tomography (CBCT) scans acquired under weight-bearing conditions, since it leads to severe artifacts in reconstructions. In knee imaging, a state-of-the-art approach to compensate for patient motion uses fiducial markers attached to the skin. However, marker placement is a tedious and time consuming procedure for both, the physician and the patient. In this manuscript we investigate the use of anatomical landmarks in an attempt to replace externally attached fiducial markers. To this end, we devise a method to automatically detect anatomical landmarks in projection domain X-ray images irrespective of the viewing direction. To overcome the need for annotation of every X-ray image and to assure consistent annotation across images from the same subject, annotations and projection images are generated from 3D CT data. Twelve landmarks are annotated in supine CBCT reconstructions of the knee joint and then propagated to synthetically generated projection images. Then, a sequential Convolutional Neuronal Network is trained to predict the desired landmarks in projection images. The network is evaluated on synthetic images and real clinical data. On synthetic data promising results are achieved with a mean prediction error of \(8.4 \pm 8.2\) pixel. The network generalizes to real clinical data without the need of re-training. However, practical issues, such as the second leg entering the field of view, limit the performance of the method at this stage. Nevertheless, our results are promising and encourage further investigations on the use of anatomical landmarks for motion management.