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<p>Medical imaging with its high resolution and detailed anatomical insights plays a crucial role in early cancer detection and diagnosis. However, achieving precise segmentation remains challenging, particularly with conventional deep learning models that demand substantial computational resources. To enhance the efficiency of cancer region segmentation, we propose a lightweight Generative Adversarial Network (GAN) model, based on a modified pix2pix architecture. The generator and discriminator in this architecture are optimized using group and spectral normalization techniques, improving gradient propagation while reducing model complexity and computational overhead. The proposed approach is evaluated on two distinct medical modalities: microultrasound for prostate and contrast-enhanced Magnetic Resonance Imaging (MRI) for hepatocellular carcinoma (HCC) segmentation. The performance of the proposed model is evaluated using the Dice coefficient and pixel accuracy, showing significantly higher values compared to conventional segmentation models for both prostate and HCC across two different imaging modalities. Furthermore, the proposed model achieves faster training times with improved efficiency, which is 17.8% faster than the conventional segmentation model. It underscores the potential of lightweight GANs as a viable alternative for high-precision medical image segmentation, particularly in resource-constrained environments.</p>

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Lightweight GANs in domain generalized medical image segmentation

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Kishan Madhav AG,
  • Avinash Venugopal,
  • Muthu Subash Kavitha,
  • Syed Ibrahim SP

摘要

Medical imaging with its high resolution and detailed anatomical insights plays a crucial role in early cancer detection and diagnosis. However, achieving precise segmentation remains challenging, particularly with conventional deep learning models that demand substantial computational resources. To enhance the efficiency of cancer region segmentation, we propose a lightweight Generative Adversarial Network (GAN) model, based on a modified pix2pix architecture. The generator and discriminator in this architecture are optimized using group and spectral normalization techniques, improving gradient propagation while reducing model complexity and computational overhead. The proposed approach is evaluated on two distinct medical modalities: microultrasound for prostate and contrast-enhanced Magnetic Resonance Imaging (MRI) for hepatocellular carcinoma (HCC) segmentation. The performance of the proposed model is evaluated using the Dice coefficient and pixel accuracy, showing significantly higher values compared to conventional segmentation models for both prostate and HCC across two different imaging modalities. Furthermore, the proposed model achieves faster training times with improved efficiency, which is 17.8% faster than the conventional segmentation model. It underscores the potential of lightweight GANs as a viable alternative for high-precision medical image segmentation, particularly in resource-constrained environments.