融合集成学习与3D生成模型，实现脑肿瘤精准分割与真实组织重建。
Ensemble Learning and 3D Pix2Pix for Comprehensive Brain Tumor Analysis in Multimodal MRI
- 用混合注意力机制的Transformer与CNN结合做肿瘤分割，提升空间关系建模能力。
- 在BraTS 2023挑战中，分割Dice达0.91，生成图像SSIM超0.95，结果接近真实组织。
- 适合医学影像研究者、临床辅助诊断系统开发者参考，尤其关注多模态脑肿瘤分析。
针对多模态MRI中胶质瘤病灶的分割与修复需求，本文提出一种整合集成学习与混合变换器模型及卷积神经网络（CNN）的方法，并创新性应用3D Pix2Pix生成对抗网络（GAN）。该方法结合轴向注意力与变换器编码器增强空间关系建模，实现精确肿瘤分割；同时利用3D Pix2Pix GAN合成生物学上合理的脑组织。在应对BraTS 2023挑战时，该方法在不同肿瘤类型和子区域均表现优异，量化评估显示分割的骰子相似系数（DSC）达0.91，豪斯多夫距离（HD95）为4.78，生成图像的结构相似性指数（SSIM）超过0.95，峰值信噪比（PSNR）达29.6，均方误差（MSE）为0.003。定性评估也证实输出具有临床相关性。结果表明，先进机器学习技术的融合为脑肿瘤全面分析提供了新路径，有望推动医学影像中临床决策与患者管理的进步。
原文摘要 · Abstract (English)
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.
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