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PET/CT Cross-modal medical image fusion of lung tumors based on DCIF-GAN
Information Sciences | 更新时间：2024-02-01
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    • PET/CT Cross-modal medical image fusion of lung tumors based on DCIF-GAN

    • “在医学图像融合领域取得了一项重要研究成果。针对现有基于生成对抗网络（GAN）的医学图像融合方法存在的训练不稳定、提取图像语义信息能力不足等问题，研究团队提出了一种双耦合交互式融合GAN（DCIF-GAN）。这一创新方案在融合肺部肿瘤PET/CT医学图像时表现出色，有效提升了图像质量。DCIF-GAN通过设计双生成器双鉴别器结构，实现生成器与鉴别器之间的耦合，并通过全局自注意力机制强化交互式融合。同时，引入耦合CNN-Transformer的特征提取和重构模块，显著提高了对同一模态图像内部局部和全局特征信息的提取能力。此外，跨模态交互式融合模块的应用，进一步整合了不同模态间的全局交互信息。实验验证表明，DCIF-GAN在平均梯度、空间频率、结构相似度、标准差、峰值信噪比和信息熵等指标上，相比其他四种方法中最优方法，分别提高了1.38%、0.39%、29.05%、30.23%、0.18%和4.63%。这一成果不仅突出了病变区域信息，还使融合图像结构更加清晰，纹理细节更丰富。这一研究为医学图像融合领域提供了新的解决方案，有望为计算机辅助诊断技术的发展开辟新方向。”
      ”
    • Optics and Precision Engineering   Vol. 32, Issue 2, Pages: 221-236(2024)

    • DOI：10.37188/OPE.20243202.0221    

      CLC： TP399

    • Published：25 January 2024，

      Received：02 August 2023，

      Revised：14 September 2023，

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  • ZHOU Tao,CHENG Qianru,ZHANG Xiangxiang,et al.PET/CT Cross-modal medical image fusion of lung tumors based on DCIF-GAN[J].Optics and Precision Engineering,2024,32(02):221-236. DOI： 10.37188/OPE.20243202.0221.

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