Skeletal bone age assessment based on deep convolutional neural networks

Abstract

Bone Age Assessment (BAA) is a pediatric examination performed to determine the difference between children’s skeletal bone age and chronological age, the inconsistency between the two will often indicate either hormonal problems or abnormalities in the skeletal system maturity. Previous works to upgrade the tedious traditional techniques had failed to address the human expert inter-observer variability in order to significantly refine BAA evaluations. This paper proposes a deep learning method that detects and segments carpal bones as the region of interests within the left hand and wrist radiographs, and then feed the image data into a deep convolutional neural network. Tests are then made to determine whether it is more efficient to use full hand radiographs or segmented regions of interest, and also made comparisons with some CNN models. Evaluations show that the proposed method can dramatically increase the accuracy.