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Related Experiment Video

Updated: Oct 8, 2025

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Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial

Kyungsoo Bae1,2, Dong Yul Oh3, Il Dong Yun4

  • 1Department of Radiology, Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju, Korea.

Korean Journal of Radiology
|January 4, 2022
PubMed
Summary

Deep learning-based bone suppression imaging (BSp-DL) shows comparable performance to dual-energy bone subtraction (BSt-DE) for detecting pulmonary nodules on chest radiographs. Both advanced techniques significantly improve radiologists' nodule detection capabilities compared to standard radiography.

Keywords:
Bone suppression imagingChest radiographyDeep learningGenerative adversarial networkPulmonary nodules

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Diagnostic Imaging Techniques

Background:

  • Pulmonary nodule detection on chest radiographs (CXRs) is crucial for early lung cancer diagnosis.
  • Overlapping bones on CXRs can obscure small pulmonary nodules, challenging radiologists.
  • Deep learning (DL) and dual-energy (DE) techniques offer potential solutions for bone suppression in CXRs.

Purpose of the Study:

  • To compare the efficacy of generative adversarial network (GAN)-based deep learning bone suppression (BSp-DL) with dual-energy bone subtraction (BSt-DE) for pulmonary nodule detection.
  • To evaluate the impact of these bone suppression techniques on radiologists' diagnostic performance on CXRs.

Main Methods:

  • A study involving 111 adults, with 49 patients confirmed to have 83 pulmonary nodules via CT scan.
  • Two radiologists assessed CXRs under three conditions: standard, BSt-DE, and BSp-DL, using CT as the reference standard.
  • Performance was evaluated using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) analyses, with subgroup analyses for nodule characteristics.

Main Results:

  • Both BSt-DE and BSp-DL significantly outperformed standard CXR in nodule-wise and person-wise detection (p ≤ 0.005).
  • BSp-DL demonstrated comparable person-wise performance to BSt-DE (p ≥ 0.064).
  • Both techniques improved detection of nodules overlapping with bones and in the upper/middle lung zones (p < 0.017), while BSt-DE excelled in detecting peripheral and sub-centimeter nodules (p < 0.017).

Conclusions:

  • GAN-based BSp-DL offers comparable performance to BSt-DE and enhances radiologists' ability to detect pulmonary nodules on CXRs.
  • While BSp-DL shows promise, further technical advancements are needed for optimal delineation of small and peripheral nodules.
  • Both BSp-DL and BSt-DE represent significant improvements over standard CXR for pulmonary nodule detection.