# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
###### Abstract
Figure 1 shows the regions of interest of the TW method, where the primary areas are the epiphysis and metaphysis of carpal and phalanges bones. To be specific, only phalanges from three fingers are analyzed, which are thumb, middle and pinky fingers. The final bone age assessment is calculated through scores summation of all regions of interest. Even though the observer bias is reduced through observing dedicated regions using the TW method, it still requires extensive experience for the doctors to accurately predict the bone age. Hence, a computer-assisted system will be a good supplement tool in helping them to do the prediction. In the early development of automated bone age assessment systems, conventional machine learning approaches such as neural networks (NN) [8] and support vector regression (SVR) [9] have been extensively used to predict the bone age while using the handcrafted features. These types of features are manually engineered, which are not optimally designed with regard to the problem. Hence, a deep learning approach has been proposed in [10] to optimally learn the unique features, which are then utilized for the skeletal bone age prediction based on hand X-ray images. The deep learning approach  
Figure 1: Regions of interest used in Tanner–Whitehouse assessment, which are indicated by the red and purple bounding boxes. The red boxes represent the regions of interest of the phalanges bones, while the purple box indicates the region of interest for the carpal bones. mainly uses convolutional neural networks to extract the spatial information from various filters of various network levels, which is then classified or regressed while using dense neural network layers. This architecture has been successfully implemented in many biomedical applications that include eye disease detection [11], Alzheimer diagnosis [12], COVID-19 screening [13], physiotherapy [14], and cardiac analysis [15]. However, the input images will come in various sizes and conditions, where some images will be relatively small for the newborn baby and vice versa for the late teen case. Moreover, there is no standardization in the hand pose while capturing the X-ray images, where the hand might tilt at certain angles. Therefore, we propose an image registration approach while using a composite function of separable convolutional neural network-based segmentation and keypoints detector to realign the images into a standard representation. The main advantage of the proposed approach is the lightweight nature of the networks that use separable convolution as the core operator, where the computational burden is lesser when compared to the full convolution architecture. The hand segmentation network is based on DeepLab V3 plus [16] and the keypoints regressor network is based on MobileNet V1 [17]. In the end, we apply a residual separable convolution scheme using Xception [18] network to train a regressor for an accurate bone age prediction that produces a mean absolute error value of fewer than 0.7 years. This network uses a unique three-layer residual separable convolution that reduces the probability of diminishing gradient issues during the training phase. Moreover, it is also in tune with the findings presented in [19], where the network should be designed, such that the depth and width of the filters are proportionally balanced to give the best classification or regression performance. The proposed system that utilized separable convolution extensively has been tested on X-ray images database that covers an age range of 1 to 228 months old. A large dataset allows for the network to learn better in generalizing the mapping between the X-ray images to the predicted age. Thus, several works [20; 21] that are based on a small dataset will not be considered as the benchmark methods and, hence, all performance comparisons are benchmarked with the state-of-the-art deep learning networks. Data augmentation has also been applied to further improve the training process by using shearing, flipping, and contrast variation operations.