迈向无标签脑肿瘤分割：基于多模态MRI的无监督学习
Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRI
AI总结：
提出仅用健康脑MRI训练的多模态视觉Transformer自编码器，通过重建误差图实现无标签脑肿瘤分割，在BraTS-GoAT 2025数据集上取得临床可行的定位效果。
摘要：
无监督异常检测（UAD）为磁共振成像（MRI）中的脑肿瘤分割提供了一种有监督学习的补充替代方案，尤其是在标注数据集有限、成本高昂或不一致的情况下。在本工作中，我们提出了一种新颖的多模态视觉Transformer自编码器（MViT-AE），该模型仅使用健康脑部MRI进行训练，通过基于重建的误差图来检测和定位肿瘤。这种无监督范式使得分割无需依赖人工标签，解决了神经影像工作流程中一个关键的可扩展性瓶颈。我们的方法在BraTS-GoAT 2025 Lighthouse数据集上进行了评估，该数据集包含多种类型的肿瘤，如胶质瘤、脑膜瘤和儿童脑肿瘤。为了提升性能，我们引入了一种多模态早期-晚期融合策略，利用多个MRI序列之间的互补信息，并构建了一个后处理流程，集成Segment Anything Model（SAM）来细化预测的肿瘤轮廓。尽管UAD存在已知的挑战，特别是在检测小病灶或非增强病灶方面，我们的方法仍实现了具有临床意义的肿瘤定位，在测试集上的病灶级Dice相似系数为0.437（全肿瘤）、0.316（肿瘤核心）和0.350（增强肿瘤），在验证集上的异常检出率为89.4%。这些发现凸显了基于Transformer的无监督模型作为神经肿瘤影像学中可扩展、标签高效工具的潜力。
英文摘要：
Unsupervised anomaly detection (UAD) presents a complementary alternative to supervised learning for brain tumor segmentation in magnetic resonance imaging (MRI), particularly when annotated datasets are limited, costly, or inconsistent. In this work, we propose a novel Multimodal Vision Transformer Autoencoder (MViT-AE) trained exclusively on healthy brain MRIs to detect and localize tumors via reconstruction-based error maps. This unsupervised paradigm enables segmentation without reliance on manual labels, addressing a key scalability bottleneck in neuroimaging workflows. Our method is evaluated in the BraTS-GoAT 2025 Lighthouse dataset, which includes various types of tumors such as gliomas, meningiomas, and pediatric brain tumors. To enhance performance, we introduce a multimodal early-late fusion strategy that leverages complementary information across multiple MRI sequences, and a post-processing pipeline that integrates the Segment Anything Model (SAM) to refine predicted tumor contours. Despite the known challenges of UAD, particularly in detecting small or non-enhancing lesions, our method achieves clinically meaningful tumor localization, with lesion-wise Dice Similarity Coefficient of 0.437 (Whole Tumor), 0.316 (Tumor Core), and 0.350 (Enhancing Tumor) on the test set, and an anomaly Detection Rate of 89.4% on the validation set. These findings highlight the potential of transformer-based unsupervised models to serve as scalable, label-efficient tools for neuro-oncological imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
