Title
Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment from Hand Radiograph.
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.
Year
DOI
Venue
2019
10.1109/JBHI.2018.2876916
IEEE journal of biomedical and health informatics
Keywords
Field
DocType
Bones,Training,X-ray imaging,Machine learning,Feature extraction,Radiography,Convolutional neural networks
Bone age assessment,Bone age,Regression,Pattern recognition,Convolutional neural network,Computer science,Outlier,Feature extraction,Artificial intelligence,Radiography,Deep learning
Journal
Volume
Issue
ISSN
23
5
2168-2208
Citations 
PageRank 
References 
3
0.37
0
Authors
11
Name
Order
Citations
PageRank
Xuhua Ren1172.39
Tingting Li230.37
Xiujun Yang330.37
Shuai Wang4242.98
Sahar Ahmad5128.33
Lei Xiang617212.47
Shaun Richard Stone730.37
Lihong Li860.87
Yiqiang Zhan985958.54
Dinggang Shen107837611.27
Qian Wang1153654.97