Bone age assessment gives us evidence to analyze the children growth status and the rejuvenation involved chronological and biological ages. All the previous works consider left-hand X-ray image of a child in their works. In this paper, we carry out a study on estimating human age using whole-body bone CT images and a novel convolutional neural network. Our model with additional connections shows an effective way to generate a massive number of vital features while reducing overfitting influence on small training data in the medical image analysis research area. A dataset and a comparison with common deep architectures will be provided for future research in this field.
@article{arxiv.1901.10237,
title = {Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features},
author = {Hai-Duong Nguyen and Soo-Hyung Kim},
journal= {arXiv preprint arXiv:1901.10237},
year = {2019}
}