Multi-Characteristic Reinforcement of Horizontally Integrated TENet Based on Wrist Bone Development Criteria for Pediatric Bone Age Assessment

Pediatric bone age assessment (BAA) is an essential human physiological examination that reflects human growth potential and sexual maturation trends. In clinical practice, the "Methodology for Bone Maturation and Evaluation of the Wrist in Chinese Adolescent Children" (CHN-05) is a widely used method for BAA by Chinese radiologists. CHN-05 adopts the metacarpal length (ML) as well as the metacarpal margin (MM) as reference standards to estimate bone age. Inspired by the semantic description of CHN-05, we propose a new model, called a topology and edge map composed of a network(TENet), for automatic bone age assessment. In TENet, we design a hand topology module to recognize key hand locations and extract structural semantics. In addition, we devise an edge feature enhancement module to supply precise skeletal edge information throughout the training process. Our model can detect the overall message of edges alongside the local message of topology for the purpose of multi-feature horizontally fused assessment of bone age. Experimental results show that our TENet achieves a state-of-the-art model performance of 5.35 mean absolute error (MAE) on the public dataset RSNA. Since our model for designing follows CHN-05 semantic logic, it is reliable and interpretable in terms of clinical use.