TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks

Abstract

Deep learning technology has rapidly evolved in recent years. Bone age assessment (BAA) is a typical object detection and classification problem that would benefit from deep learning. Convolutional neural networks (CNNs) and their variants are hence increasingly used for automating BAA, and they have shown promising results. In this paper, we propose a complete end-to-end BAA system to automate the entire process of the Tanner-Whitehouse 3 method, starting from localization of the epiphysis-metaphysis growth regions within 13 different bones and ending with estimation of the corresponding BA. Specific modifications to the CNNs and other stages are proposed to improve results. In addition, an annotated database of 3300 X-ray images is built to train and evaluate the system. The experimental results show that the average top-1 and top-2 prediction accuracies for skeletal bone maturity levels for 13 regions of interest are 79.6% and 97.2%, respectively. The mean absolute error and root mean squared error in age prediction are 0.46 years and 0.62 years, respectively, and accuracy within one year of the ground truth of 97.6% is achieved. The proposed system is shown to outperform a commercially available Greulich-Pyle-based system, demonstrating the potential for practical clinical use. INDEX TERMS Bone age assessment, deep learning, GP, TW3.

Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks

“…2,[13][14][15] However, the majority of existing deep learning-based BAA systems are based on the GP method and are potentially vulnerable to the low repeatability of measurements and the systematic errors that are inherent to the GP method. 16 Therefore, a TW3-based BAA system using deep neural networks was developed to automate the entire process, from the localization of the 13 epiphysis-metaphysis growth regions to the output of the estimated bone age. 16 The software was trained to use the TW3 method to automatically analyze hand-wrist radiographs entered in the form of image files and to present bone ages in 0.1 years.…”

“…The details of the system have been previously described. 16 After a hand-wrist radiograph (in the JPG file format) was entered into the BAA software and a rough area containing 13 ROIs was selected using a computer mouse, assessment was activated. Upon completion of the process after a few seconds, the predicted bone age was displayed along with skeletal maturity ratings of each of the 13 ROIs (Fig.…”

“…However, the majority of existing deep learning-based BAA systems are based on the GP method and are potentially vulnerable to the low repeatability of measurements and the systematic errors that are inherent to the GP method. 16 …”

“…Therefore, a TW3-based BAA system using deep neural networks was developed to automate the entire process, from the localization of the 13 epiphysis-metaphysis growth regions to the output of the estimated bone age. 16 The software was trained to use the TW3 method to automatically analyze hand-wrist radiographs entered in the form of image files and to present bone ages in 0.1 years. It was aimed to utilize the BAA system to provide more efficient evaluation of skeletal maturity in clinical practice.…”

Multi Region-Based Feature Connected Layer (RB-FCL) of deep learning models for bone age assessment

“…Son et al added to the automatic of the Tanner Whithouse (TW 3) strategy, which is a reference in bone age evaluation [20]. Confinement of the bone epiphysis and metaphysis was done to estimate the age of the bone.…”