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Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

Abstract

Objective

To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images.

Materials and methods

A total of 1449 patients were used for experiments and analysis in this retrospective study, who underwent spinal or abdominal CT scans for other indications between March 2018 and May 2020. All data was gathered from three different CT vendors. Among them, 586 cases were used for training, and other 863 cases were used for testing. A fully convolutional neural network, called U-Net, was employed for automated vertebral body segmentation. The manually sketched region of vertebral body was used as the ground truth for comparison. A convolutional neural network, called DenseNet-121, was applied for BMD calculation. The values post-processed by quantitative computed tomography (QCT) were identified as the standards for analysis.

Results

Based on the diversity of CT vendors, all testing cases were split into three testing cohorts: Test set 1 (n = 463), test set 2 (n = 200), and test set 3 (n = 200). Automated segmentation correlated well with manual segmentation regarding four lumbar vertebral bodies (L1–L4): the minimum average dice coefficients for three testing sets were 0.823, 0.786, and 0.782, respectively. For testing sets from different vendors, the average BMDs calculated by automated regression showed high correlation (r > 0.98) and agreement with those derived from QCT.

Conclusions

A deep learning–based method could achieve fully automatic identification of osteoporosis, osteopenia, and normal bone mineral density in CT images.

Key Points

• Deep learning can perform accurate fully automated segmentation of lumbar vertebral body in CT images.

• The average BMDs obtained by deep learning highly correlates with ones derived from QCT.

• The deep learning–based method could be helpful for clinicians in opportunistic osteoporosis screening in spinal or abdominal CT scans.

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Abbreviations

ACR:

American College of Radiology

BMD:

Bone mineral density

DCNN:

Deep convolutional neural network

DSC:

Dice similarity coefficient

DXA:

Dual-energy X-ray absorptiometry

ESP:

European spine phantom

HU :

Hounsfield units

ISCD:

International Society for Clinical Densitometry

PET:

Positron emission tomography

PPV:

Positive predictive value

QCT:

Quantitative computed tomography

QUS:

Quantitative ultrasound

WL:

Window level

WW:

Window width

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Funding

This study has received funding by Science and Technology Planning Project of Zhuhai (ZH2202200010HJL), by Investigator-Initiated Clinical Trial of The Fifth Affiliated Hospital of Sun Yat-sen University (YNZZ003).

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Correspondence to Guobin Hong or Shaolin Li.

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The scientific guarantor of this publication is Shaolin Li.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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No complex statistical methods were necessary for this paper.

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• retrospective

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• performed at one institution

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Fang, Y., Li, W., Chen, X. et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks. Eur Radiol 31, 1831–1842 (2021). https://doi.org/10.1007/s00330-020-07312-8

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  • DOI: https://doi.org/10.1007/s00330-020-07312-8

Keywords

  • Deep learning
  • Spine
  • Osteoporosis
  • Bone density
  • Tomography, X-ray computed