Motivated by the need for advanced solutions in the segmentation and inpainting of glioma-affected brain regions in multi-modal magnetic resonance imaging (MRI), this study presents an integrated approach leveraging the strengths of ensemble learning with hybrid transformer models and convolutional neural networks (CNNs), alongside the innovative application of 3D Pix2Pix Generative Adversarial Network (GAN). Our methodology combines robust tumor segmentation capabilities, utilizing axial attention and transformer encoders for enhanced spatial relationship modeling, with the ability to synthesize biologically plausible brain tissue through 3D Pix2Pix GAN. This integrated approach addresses the BraTS 2023 cluster challenges by offering precise segmentation and realistic inpainting, tailored for diverse tumor types and sub-regions. The results demonstrate outstanding performance, evidenced by quantitative evaluations such as the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95) for segmentation, and Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean-Square Error (MSE) for inpainting. Qualitative assessments further validate the high-quality, clinically relevant outputs. In conclusion, this study underscores the potential of combining advanced machine learning techniques for comprehensive brain tumor analysis, promising significant advancements in clinical decision-making and patient care within the realm of medical imaging.

翻译：本研究受多模态磁共振成像（MRI）中胶质瘤影响脑区分割与修复的先进需求驱动，提出一种集成方法，该方法融合了混合Transformer模型与卷积神经网络（CNN）的集成学习优势，并创新性地应用了三维Pix2Pix生成对抗网络（GAN）。我们的方法结合了强大的肿瘤分割能力（利用轴向注意力与Transformer编码器以增强空间关系建模）以及通过三维Pix2Pix GAN合成生物学合理脑组织的能力。这一集成方案通过提供针对不同肿瘤类型及子区域的精确分割与逼真修复，应对了BraTS 2023集群挑战。定量评估结果证明了其卓越性能，包括用于分割的Dice相似系数（DSC）、豪斯多夫距离（HD95），以及用于修复的结构相似性指数（SSIM）、峰值信噪比（PSNR）和均方误差（MSE）。定性评估进一步验证了其高质量、具有临床相关性的输出。总之，本研究强调了结合先进机器学习技术进行全面脑肿瘤分析的潜力，有望在医学影像领域的临床决策与患者护理方面取得显著进展。