Medical Image Synthesis (MIS) plays an important role in the intelligent medical field, which greatly saves the economic and time costs of medical diagnosis. However, due to the complexity of medical images and similar characteristics of different tissue cells, existing methods face great challenges in meeting their biological consistency. To this end, we propose the Hybrid Augmented Generative Adversarial Network (HAGAN) to maintain the authenticity of structural texture and tissue cells. HAGAN contains Attention Mixed (AttnMix) Generator, Hierarchical Discriminator and Reverse Skip Connection between Discriminator and Generator. The AttnMix consistency differentiable regularization encourages the perception in structural and textural variations between real and fake images, which improves the pathological integrity of synthetic images and the accuracy of features in local areas. The Hierarchical Discriminator introduces pixel-by-pixel discriminant feedback to generator for enhancing the saliency and discriminance of global and local details simultaneously. The Reverse Skip Connection further improves the accuracy for fine details by fusing real and synthetic distribution features. Our experimental evaluations on three datasets of different scales, i.e., COVID-CT, ACDC and BraTS2018, demonstrate that HAGAN outperforms the existing methods and achieves state-of-the-art performance in both high-resolution and low-resolution.

翻译：医学图像合成在智能医疗领域中扮演着重要角色，它能显著降低医疗诊断的经济和时间成本。然而，由于医学图像的复杂性和不同组织细胞的相似特征，现有方法在满足生物一致性方面面临巨大挑战。为此，我们提出混合增强生成对抗网络（HAGAN）以保持结构纹理和组织细胞的真实性。HAGAN包含注意力混合（AttnMix）生成器、层级鉴别器以及鉴别器与生成器之间的反向跳跃连接。AttnMix一致性可微正则化能够增强真实与虚假图像之间结构纹理变化的感知能力，从而提升合成图像的病理完整性和局部区域特征的准确性。层级鉴别器通过向生成器提供逐像素的判别反馈，同步增强全局和局部细节的显著性与判别性。反向跳跃连接则通过融合真实与合成分布特征，进一步提高了精细细节的精度。我们在三个不同规模的数据集（COVID-CT、ACDC和BraTS2018）上的实验评估表明，HAGAN在高低分辨率场景下均优于现有方法，取得了最先进的性能。