  Bone age is an important measure for assessing the skeletal and biological
maturity of children. Delayed or increased bone age is a serious concern for
pediatricians, and needs to be accurately assessed in a bid to determine
whether bone maturity is occurring at a rate consistent with chronological age.
In this paper, we introduce a unified deep learning framework for bone age
assessment using instance segmentation and ridge regression. The proposed
approach consists of two integrated stages. In the first stage, we employ an
image annotation and segmentation model to annotate and segment the hand from
the radiographic image, followed by background removal. In the second stage, we
design a regression neural network architecture composed of a pre-trained
convolutional neural network for learning salient features from the segmented
pediatric hand radiographs and a ridge regression output layer for predicting
the bone age. Experimental evaluation on a dataset of hand radiographs
demonstrates the competitive performance of our approach in comparison with
existing deep learning based methods for bone age assessment.
