Abstract
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, 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 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 language | English |
|---|---|
| Journal | IEEE Journal of Biomedical and Health Informatics |
| DOIs | |
| Publication status | Accepted/In press - 2018 Jan 1 |
| Externally published | Yes |
Fingerprint
Keywords
- Bone age assessment
- Bones
- Convolutional neural networks
- deep learning
- Feature extraction
- hand radiograph
- Machine learning
- Radiography
- regression convolutional neural network
- Training
- X-ray imaging
ASJC Scopus subject areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management
Cite this
Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment from Hand Radiograph. / Ren, Xuhua; Li, Tingting; Yang, Xiujun; Wang, Shuai; Ahmad, Sahar; Xiang, Lei; Stone, Shaun Richard; Li, Lihong; Zhan, Yiqiang; Shen, Dinggang; Wang, Qian.
In: IEEE Journal of Biomedical and Health Informatics, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment from Hand Radiograph
AU - Ren, Xuhua
AU - Li, Tingting
AU - Yang, Xiujun
AU - Wang, Shuai
AU - Ahmad, Sahar
AU - Xiang, Lei
AU - Stone, Shaun Richard
AU - Li, Lihong
AU - Zhan, Yiqiang
AU - Shen, Dinggang
AU - Wang, Qian
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Skeletal bone age assessment is a common clinical practice to investigate endocrinology, 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 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.
AB - Skeletal bone age assessment is a common clinical practice to investigate endocrinology, 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 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.
KW - Bone age assessment
KW - Bones
KW - Convolutional neural networks
KW - deep learning
KW - Feature extraction
KW - hand radiograph
KW - Machine learning
KW - Radiography
KW - regression convolutional neural network
KW - Training
KW - X-ray imaging
UR - http://www.scopus.com/inward/record.url?scp=85055215069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055215069&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2018.2876916
DO - 10.1109/JBHI.2018.2876916
M3 - Article
AN - SCOPUS:85055215069
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
ER -