Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment from Hand Radiograph

Xuhua Ren, Tingting Li, Xiujun Yang, Shuai Wang, Sahar Ahmad, Lei Xiang, Shaun Richard Stone, Lihong Li, Yiqiang Zhan, Dinggang Shen, Qian Wang

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)

Abstract

Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to inter-observer variability. This advocates the need of a fully automated method for bone age assessment. We propose a regression convolutional neural network (CNN) to automatically assess the pediatric bone age from hand radiograph. Our network is specifically trained to place more attention to those bone age related regions in the X-ray images. Specifically, we first adopt the attention module to process all images and generate the coarse/fine attention maps as inputs for the regression network. Then, the regression CNN follows the supervision of the dynamic attention loss during training; thus, it can estimate the bone age of the hard (or 'outlier') images more accurately. The experimental results show that our method achieves an average discrepancy of 5.2-5.3 months between clinical and automatic bone age evaluations on two large datasets. In conclusion, we propose a fully automated deep learning solution to process X-ray images of the hand for bone age assessment, with the accuracy comparable to human experts but with much better efficiency.

Original languageEnglish
Article number8500181
Pages (from-to)2030-2038
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number5
DOIs
Publication statusPublished - 2019 Sept

Bibliographical note

Funding Information:
Manuscript received July 2, 2018; revised September 11, 2018; accepted October 13, 2018. Date of publication October 19, 2018; date of current version September 4, 2019. This research was supported in part by the Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University under Grant YG2017ZD08, in part by the National Natural Science Foundation of China under Grants 81471733 and 61471390, in part by the National Key R&D Program of China under Grant 2017YFC0107602, and in part by the Science and Technology Commission of Shanghai Municipality under Grant 16410722400. (Corresponding authors: Xiujun Yang, Dinggang Shen and Qian Wang.) X. Ren, L. Xiang, Y. Zhan, and Q. Wang are with the Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China (e-mail:,renxuhua@sjtu. edu.cn; xianglei_15@sjtu.edu.cn; yiqiang@gmail.com; wang.qian@sjtu. edu.cn).

Funding Information:
This research was supported in part by the Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University under Grant YG2017ZD08, in part by the National Natural Science Foundation of China under Grants 81471733 and 61471390, in part by the National Key R&D Program of China under Grant 2017YFC0107602, and in part by the Science and Technology Commission of Shanghai Municipality under Grant 16410722400.

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Bone age assessment
  • deep learning
  • hand radiograph
  • regression convolutional neural network

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications

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