# Detection of Pediatric Femur Configuration on X-ray Images
### Related Work  
In recent years, CNN image processing has been successfully applied in many applications, e.g., road detection and face recognition. In the case of medical images, the input data possess less salient features than typical CNN input images. The example image frame, considered in this study, with speeded-up robust features (SURF) [4] denoted as red circles are presented in Figure 1a. Note the difference in feature number in contrast to example images from datasets used in different applications, presented in Figure 1b-d. As a side note, the SURF features are presented in Figure 1 for comparison reasons. Any other traditional gradient-based method of feature extraction would result in a similar result.  
Due to the complex (and unique) nature of the medical images, most CNN applications in image processing involve classification [8; 9]. Since classification output is discrete (i.e., classes) it is considered less difficult than regression, where output is usually a real number (keypoint positions, segmentation, object detection, etc.). Although several CNN-based keypoint detection methods have been proposed in medical image analyses [10; 11; 12], it is still challenging to detect image keypoints.  
Interestingly, several deep learning algorithms had been used on adult X-ray images [13; 14; 15; 16]. Meanwhile, very little research was conducted for medical image data collected for children [17]. Plenty of reasons for this imbalance can be named, e.g., consent problems, complex nature of children's medical images (age dependency of visible structures, intra- and interpopulation variation).  
Recently, individual studies have made attempts to apply CNN to solve regression tasks for children's medical images [18; 19; 20]. Nevertheless, there have been issues considering the lack of input data, as pediatric medical image datasets are rarely publicly available.  
To avoid the problem of limited training data, some deep learning based keypoint detection methods adopt local image patches as samples to perform regression for each of the patches  
Figure 1: Example images with SURF features. (**a**) X-ray image; (**b**) Dogs vs. Cats [5]; (**c**) KITTI dataset [6]; (**d**) MNIST dataset [7].  
individually [21]. Those solutions are time consuming and require large computational costs, if each landmark is detected separately.  
Alternative solutions use end-to-end learning strategies with entire images as input and the keypoint coordinates as output [22]. The keypoints can be represented as heatmaps [12], i.e., images where Gaussians are located at the position of the keypoints. Then, the task can be understood as image segmentation, with heatmaps being the target. This opens plenty of new possibilities, as many network architectures are designed for image segmentation, e.g., U-Net [23].  
The complexity of pediatrics medical images, in comparison to adult ones, is specifically evident in knee radiographs. The images of younger patients have open growth plates, ossification center changes, and possess less characteristic radiographic landmarks [24]. For example, the contact points of knee joint surfaces [25] are not detectable in the X-ray images of young patients. Given this troublesome characteristic of input data, the task of keypoint detection is more demanding, which has to be encountered in the algorithm design.