Paper Info
Title
Semi-supervised Learning for Bone Mineral Density Estimation in Hip X-Ray Images
Abstract
Bone mineral density (BMD) is a clinically critical indicator of osteoporosis, usually measured by dual-energy X-ray absorptiometry (DEXA). Due to the limited accessibility of DEXA machines and examinations, osteoporosis is often under-diagnosed and under-treated, leading to increased fragility fracture risks. Thus it is highly desirable to obtain BMDs with alternative cost-effective and more accessible medical imaging examinations such as X-ray plain films. In this work, we formulate the BMD estimation from plain hip X-ray images as a regression problem. Specifically, we propose a new semi-supervised self-training algorithm to train a BMD regression model using images coupled with DEXA measured BMDs and unlabeled images with pseudo BMDs. Pseudo BMDs are generated and refined iteratively for unlabeled images during self-training. We also present a novel adaptive triplet loss to improve the model's regression accuracy. On an in-house dataset of 1,090 images (819 unique patients), our BMD estimation method achieves a high Pearson correlation coefficient of 0.8805 to ground-truth BMDs. It offers good feasibility to use the more accessible and cheaper X-ray imaging for opportunistic osteoporosis screening.
Year
DOI
Venue
2021
10.1007/978-3-030-87240-3_4
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT V
Keywords
DocType
Volume
Bone mineral density estimation, Hip X-ray, Semi-supervised learning
Conference
12905
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Kang Zheng100.34
Yirui Wang202.03
Xiao-Yun Zhou378.03
Fakai Wang401.35
Le Lu5129786.78
Chi-Hung Lin621734.67
Lingyun Huang703.04
Guotong Xie800.68
Jing Xiao901.35
Chang-Fu Kuo1001.35
Shun Miao1114317.54