用生成对抗网络提升脑肿瘤分割精度，减少对标注数据的依赖。
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
- 结合预训练GAN与Unet，通过对抗损失迭代优化分割结果。
- 在BraTS数据集上，病变级Dice和HD95指标优于基线模型。
- 适合医疗影像分析、小样本场景下的精准分割任务。
本文提出一种新型脑肿瘤分割框架，融合预训练GAN与Unet结构。通过全局异常检测模块与精炼掩码生成网络，准确识别肿瘤敏感区域，并利用对抗损失约束实现分割精度的迭代提升。采用多模态MRI数据与合成图像增强策略，增强模型鲁棒性并缓解标注数据稀缺问题。在BraTS数据集上的实验表明，该方法在病变级Dice与HD95指标上均优于基线模型，具备良好可扩展性，显著降低对全标注数据的依赖，为临床实际应用提供可行路径。
原文摘要 · Abstract (English)
This work introduces a novel framework for brain tumor segmentation leveraging pre-trained GANs and Unet architectures. By combining a global anomaly detection module with a refined mask generation network, the proposed model accurately identifies tumor-sensitive regions and iteratively enhances segmentation precision using adversarial loss constraints. Multi-modal MRI data and synthetic image augmentation are employed to improve robustness and address the challenge of limited annotated datasets. Experimental results on the BraTS dataset demonstrate the effectiveness of the approach, achieving high sensitivity and accuracy in both lesion-wise Dice and HD95 metrics than the baseline. This scalable method minimizes the dependency on fully annotated data, paving the way for practical real-world applications in clinical settings.
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