###### Abstract
The authors state that their method is able to exploit the structural knowledge effectively and allow rich information exchange between landmarks for accurate coordinate estimation. The first GCN estimated a global transformation of the landmarks, while the second GCN determined local offsets to adjust the landmark coordinates further. [PERSON] et al., in a journal article [10], introduced a CNN architecture that learns to split the localization task into two simpler sub-problems, thus reducing the overall need for large training datasets. Their fully convolutional SpatialConfiguration-Net (SCN) utilized one component to obtain locally accurate but ambiguous candidate predictions, while the other component improved robustness to ambiguities by incorporating the spatial configuration of landmarks. Since our research is based on this method, we will provide details about the SCN in the next sections. Lastly, we expose the method by [PERSON] et al. [11]. The authors proposed the usage of an individual model for each landmark, where each model was trained by the ResNet50 architecture. These constructed models were applied to smaller patches extracted from the cephalometric image. The method assumed that each patch that was passed into the model must contain the landmark that was being detected by this model. To ensure this, each testing image was aligned to every training image by using a translational registration. Landmarks from the training image with the best fit after registration were considered as centers for the extracted patches. The results obtained on the database of public cephalograms with 19 landmarks were comparable to other state-of-the-art methods. However, this method does not scale well to a larger number of cephalometric landmarks and training images. In order for cephalometric analysis to be meaningful and useful as a diagnostic tool, it is necessary to detect as many cephalometric landmarks on the cephalogram as accurately as possible. Usage of lateral cephalograms predominates today in the field of orthodontics; therefore, we also focused on this type of cephalograms in our research (similar to the related works summarized above). The identified shortcomings of early related works indicated that these methods were adapted for a small number of cephalometric landmarksand for a small number of high-quality input images. State-of-the-art methods [8; 9; 10] are practically invariant to brightness/contrast variations, or to situations during cephalosporers' capture, respectively. Additionally, an addition of new landmarks that we would like to detect with these methods is relatively simple, as we only need to supplement the learning set and retrain the CNNs (and possibly add some channels). Although state-of-the-art methods have proven to be very effective in locating cephalometric landmarks, it should be noted that these methods have been validated on only 19 landmarks and on just some hundred testing images. Thus, a research question arises as to whether the CNN architectures of these methods have sufficient capacity to localize a larger number of landmarks effectively on a larger set of testing images captured with different X-ray devices. We are tackling a real-world problem from the field of orthodontics in this research; namely, we are developing a detection method as an enhancement of the state-of-the-art, which will be able to detect a large number of cephalometric landmarks (in our study 72) on highly variable testing images. It is understood by variability that testing images are of different sizes (and different spatial resolutions), and that they were captured by using different X-ray devices in different orthodontic clinics (most likely with different device settings). On the other hand, this research also solves one of the concrete problems of the industry (e.g., the AUDAX company). Virtually every orthodontic software includes a module for detecting cephalometric landmarks. A greater number of very precisely localized landmarks of course means better usability of such software.