Skeletal bone age assessments for young children based on regression convolutional neural networks

Abstract

Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for investigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back to 1950 were often tedious and susceptible to inter-observer variability, and preceding attempts to improve these traditional techniques have inadequately addressed the human expert inter-observer variability so as to significantly refine bone age evaluations. In this paper, an automated and efficient approach with regression convolutional neural network is proposed. This approach automatically exploits the carpal bones as the region of interest (ROI) and performs boundary extraction of carpal bones, then based on the regression convolutional neural network it evaluates the skeletal age from the left hand wrist radiograph of young children. Experiments show that the proposed method achieves an average discrepancy of 2.75 months between clinical and automatic bone age evaluations, and achieves 90.15% accuracy within 6 months from the ground truth for male. Further experimental results with test radiographs assigned an accuracy within 1 year achieved 99.43% accuracy.