Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features

TL;DR

This paper introduces a deep hierarchical neural network for automatic whole-body bone age assessment using CT images, addressing overfitting issues with a novel architecture and providing a new dataset for future research.

Contribution

It proposes a new CNN model with additional connections for whole-body bone age estimation from CT images, expanding beyond traditional hand X-ray methods.

Findings

Effective feature generation with reduced overfitting

Comparison with common deep architectures included

Provides a new dataset for future research

Abstract

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.