Bone age assessment method based on fine-grained image classification using multiple regions of interest

Bone age assessment is commonly used to determine the growth status and growth potential of children. In this paper, the bone age assessment is regarded as a fine-grained image classification problem as bone age assessment is usually performed on radiographs of the left hand. An end-to-end bone age assessment model was proposed. This model is composed of four parts: feature extractor, Region of Interest (ROI) selection subnet, guidance subnet, and assessment subnet. Feature extractor is implemented based on Convolutional Neural Networks (CNNs), ResNet50 was used to extract image features. ROI selection subnet is used to select multiple informative ROIs that contain representative images features in the radiograph. Guidance subnet can guide the ROI selection subnet to select ROI more appropriately. Assessment subnet is used for bone age assessment by utilizing the extracted image features. The proposed model can extract the most informative ROIs in the radiographs, and use these ROIs to improve the accuracy of bone age assessment. In this paper, the bone age assessment model is tested on a public data set. The experimental results show that the proposed bone age assessment model has the highest accuracy, and the Mean Absolute Error (MAE) reaches 6.65 months.