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Semi-supervised Learning for Bone Mineral Density Estimation in Hip X-Ray Images

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12905)

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.

Keywords

  • Bone mineral density estimation
  • Hip X-ray
  • Semi-supervised learning

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Notes

  1. 1.

    This automated ROI localization module is achieved by re-implementing the deep adaptive graph (DAG) network [10].

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Zheng, K. et al. (2021). Semi-supervised Learning for Bone Mineral Density Estimation in Hip X-Ray Images. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_4

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  • DOI: https://doi.org/10.1007/978-3-030-87240-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87239-7

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