用零样本融合技术生成带分割掩码的3D脑MRI，缓解标注数据少的问题
ZECO: ZeroFusion Guided 3D MRI Conditional Generation
- 通过空间变换模块压缩3D MRI到潜在空间，捕捉切片间关系
- 在有限数据上训练时，生成图像与掩码匹配度高，避免过拟合
- 适合医学影像生成、数据增强及小样本模型训练的研究者
医学图像分割对提升磁共振成像（MRI）诊断准确性和治疗规划至关重要。然而，获取精确病灶掩码用于训练需专业技能且耗时，导致临床数据集规模小。本文提出ZECO，一种基于ZeroFusion的3D MRI条件生成框架，可提取、压缩并生成高质量的含3D分割掩码的MRI图像，以缓解数据稀缺问题。为有效捕捉体数据中切片间的关联，引入空间变换模块，将MRI图像编码至紧凑潜在空间用于扩散过程。不同于无条件生成，提出的ZeroFusion方法在潜在空间中逐步将3D掩码映射至MRI图像，实现小数据集上的鲁棒训练且避免过拟合。在多种模态的脑部MRI数据集上，ZECO在定量与定性评估中均优于现有先进模型，展现出基于分割掩码生成高质量MRI图像的卓越能力。
原文摘要 · Abstract (English)
Medical image segmentation is crucial for enhancing diagnostic accuracy and treatment planning in Magnetic Resonance Imaging (MRI). However, acquiring precise lesion masks for segmentation model training demands specialized expertise and significant time investment, leading to a small dataset scale in clinical practice. In this paper, we present ZECO, a ZeroFusion guided 3D MRI conditional generation framework that extracts, compresses, and generates high-fidelity MRI images with corresponding 3D segmentation masks to mitigate data scarcity. To effectively capture inter-slice relationships within volumes, we introduce a Spatial Transformation Module that encodes MRI images into a compact latent space for the diffusion process. Moving beyond unconditional generation, our novel ZeroFusion method progressively maps 3D masks to MRI images in latent space, enabling robust training on limited datasets while avoiding overfitting. ZECO outperforms state-of-the-art models in both quantitative and qualitative evaluations on Brain MRI datasets across various modalities, showcasing its exceptional capability in synthesizing high-quality MRI images conditioned on segmentation masks.
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