  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 the 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.
