###### Abstract  
Accurate automated localization of cephalometric landmarks in skull X-ray images is the basis for planning orthodontic treatments, predicting skull growth, or diagnosing face discrepancies. Such diagnoses require as many landmarks as possible to be detected on cephalograms. Today's best methods are adapted to detect just 19 landmarks accurately in images varying not too much. This paper describes the development of the SCN-EXT convolutional neural network (CNN), which is designed to localize 72 landmarks in strongly varying images. The proposed method is based on the SpatialConfiguration-Net network, which is upgraded by adding replications of the simpler local appearance and spatial configuration components. The CNN capacity can be increased without increasing the number of free parameters simultaneously by such modification of an architecture. The successfulness of our approach was confirmed experimentally on two datasets. The SCN-EXT method was, with respect to its effectiveness, around 4% behind the state-of-the-art on the small ISBI database with 250 testing images and 19 cephalometric landmarks. On the other hand, our method surpassed the state-of-the-art on the demanding AUDAX database with 4695 highly variable testing images and 72 landmarks statistically significantly by around 3%. Increasing the CNN capacity as proposed is especially important for a small learning set and limited computer resources. Our algorithm is already utilized in orthodontic clinical practice. detection of cephalometric landmarks; skull X-ray images; convolutional neural networks; deep learning; SpatialConfiguration-Net architecture; AUDAX database +
Footnote †: journal: Applied  
from skull X-ray images (cephalograms) have emerged, based on heuristic features and rigid rules. These methods were highly dependent on the quality of the input images, and were adapted for a small number of landmarks [1] (the number of landmarks is meant here as the number of different types of landmarks we are looking for in each image). More mature methods, as well as learning-based approaches, emerged after 2010 [4; 5]. [PERSON] et al. [5; 6] proposed an efficient detection method based on Haar-like features and random forests (RFs). An RF was trained for each landmark in order to predict the more probable position of that landmark. Each tree in the RF voted for the likely new position. The RF regression-voting mechanism was integrated into the constrained local model framework that optimized a statistical shape model and total votes over all landmark positions. This detection system was adapted for the detection of 19 cephalometric landmarks. A similar method with RF and Haar-like appearance features was proposed by [PERSON] et al. in [4; 5; 7]. The difference was that a matching of the appearance shape model in a target image was sought by using a game-theoretic optimization framework. The fitted model determined the optimal landmark positions. Recently, successful methods have emerged based on convolutional neural networks (CNN) and deep learning. We expose the four best, which are comparable in effectiveness. [PERSON] et al., in a conference article [8], proposed the CNN-based architecture that consists of the pretrained VGG-19 net as a feature extraction module, an attentive feature pyramid fusion (AFPF) module, and a prediction module. They fused features from different levels in order to obtain high-resolution and semantically enhanced features in the AFPF module. A self-attention mechanism was utilized to learn corresponding weights for the fusion for different landmarks. Finally, a combination of heat maps and offset maps was employed in the prediction module to perform a pixel-wise regression-voting. The next conference paper is from [PERSON] et al. [9], who modeled landmarks as a graph and employed two global-to-local cascaded graph convolutional networks (GCNs) to reposition the landmarks towards the target locations. The graph signals of the landmarks were built by combining local image features and graph shape features.