Skeletal bone age assessments for young children based on regression convolutional neural networks.
Journal:
Mathematical biosciences and engineering : MBE
Published Date:
Jul 12, 2019
Abstract
Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations 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 suscep-tible 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 neu-ral 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.
Authors
Keywords
Adolescent
Age Determination by Skeleton
Algorithms
Carpal Bones
Child
Child, Preschool
China
Data Interpretation, Statistical
Female
Humans
Image Processing, Computer-Assisted
Infant
Infant, Newborn
Male
Neural Networks, Computer
Observer Variation
Pattern Recognition, Automated
Regression Analysis
Reproducibility of Results
X-Rays
Young Adult