  Skeletal bone age assessment (BAA), as an essential imaging examination, aims
at evaluating the biological and structural maturation of human bones. In the
clinical practice, Tanner and Whitehouse (TW2) method is a widely-used method
for radiologists to perform BAA. The TW2 method splits the hands into Region Of
Interests (ROI) and analyzes each of the anatomical ROI separately to estimate
the bone age. Because of considering the analysis of local information, the TW2
method shows accurate results in practice. Following the spirit of TW2, we
propose a novel model called Anatomical Local-Aware Network (ALA-Net) for
automatic bone age assessment. In ALA-Net, anatomical local extraction module
is introduced to learn the hand structure and extract local information.
Moreover, we design an anatomical patch training strategy to provide extra
regularization during the training process. Our model can detect the anatomical
ROIs and estimate bone age jointly in an end-to-end manner. The experimental
results show that our ALA-Net achieves a new state-of-the-art single model
performance of 3.91 mean absolute error (MAE) on the public available RSNA
dataset. Since the design of our model is well consistent with the well
recognized TW2 method, it is interpretable and reliable for clinical usage.
