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Updated: Apr 14, 2026

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GAN-based bone suppression using a combined loss function.

Lukáš Jochymek1, Markéta Vašinková1, Vít Doleží1

  • 1Faculty of Electrical Engineering and Computer Science, Department of Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic.

Frontiers in Artificial Intelligence
|April 13, 2026
PubMed
Summary

This study developed an enhanced generative adversarial network (GAN) for bone suppression in chest X-rays. The GAN significantly improved diagnostic image quality by effectively removing bone structures while preserving soft tissues.

Keywords:
U-NetX-ray image analysisautoencodersbone suppressionchest radiographscombined loss functioncomputer-aided diagnosisgenerative adversarial network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate chest radiograph analysis is crucial for diagnosing diseases like pneumonia and lung cancer.
  • Bone structures in X-rays can obscure vital soft tissues and lesions, hindering diagnosis.
  • Artificial intelligence (AI) offers various modeling paradigms for bone suppression in medical images.

Purpose of the Study:

  • To systematically evaluate different AI approaches for bone suppression in chest radiographs.
  • To compare denoising-based regression, U-Net architectures, and generative adversarial learning (GANs).
  • To propose an enhanced GAN with improved components and a combined loss function for superior bone suppression.

Main Methods:

  • Comparative methodological investigation of three AI approaches: autoencoders, U-Nets, and GANs.
  • Evaluation of different loss configurations and training regimes for reconstruction quality.
  • Development of an enhanced GAN incorporating Wasserstein, L1, perceptual, and Sobel losses.

Main Results:

  • The enhanced GAN achieved the highest performance, with a Peak Signal-to-Noise Ratio (PSNR) of 44.09 dB and a Multi-Scale Structural Similarity Index Measure (MS-SSIM) of 0.9968.
  • The proposed GAN model outperformed recently published methods on the same dataset.
  • The study demonstrated the effectiveness of the combined loss function in preserving soft-tissue appearance and structural consistency.

Conclusions:

  • The selection of the AI modeling paradigm is critical for effective bone suppression.
  • Loss function formulation significantly impacts the quality of bone suppression in chest radiographs.
  • The enhanced GAN approach offers a promising solution for improving diagnostic accuracy in chest X-ray analysis.