RAGCN: Region Aggregation Graph Convolutional Network for Bone Age Assessment From X-Ray Images

Rapid and accurate measurement of bone age from hand X-ray images is a significant task for children's maturity assessment and metabolic disorders diagnosis. With the development of deep learning technology, assessment methods based on convolutional neural networks (CNNs) have become mainstream. However, the existing CNN method generates the assessment results solely based on images, which ignores the clinical practice and thus weakens the evaluation performance. In this article, an automatic bone age assessment method based on CNN and graph convolutional network (GCN) is proposed. The overall method uses CNN for feature extraction and GCN for bone key regions inference, which mimics the physician's clinical process. Specifically, the key regions of the hand bone are first defined according to the clinical standard. Then, independent CNN pathways are established to extract the features of different key regions. Finally, a novel region aggregation GCN (RAGCN) is designed, which can aggregate the region features into the overall bone age representation according to the adjacency relation of the regions. In addition, RAGCN can also infer the importance of different regions in the feature aggregation process. The proposed method is validated on the Radiological Society of North America (RSNA) dataset and the Radiological Hand Pose Estimation (RHPE) dataset. The mean absolute error (MAE) is 4.09 months on the RSNA dataset and 6.78 months on the RHPE dataset, it is competitive and superior to other state-of-the-art methods.