用分割辅助生成缺失脑部MRI对比图像，提升肿瘤区域还原精度
Segmentation-Assisted Brain MRI Synthesis with Cross-Image Multi-Contrast Feature Memory Bank Retrieval Augmentation
- 引入分割分支反馈肿瘤掩码，引导生成聚焦病灶区域
- 通过双知识库检索增强，分别获取肿瘤上下文与全局风格信息
- 在BraTs2020和UCSF-BMSR数据集上优于现有方法
多对比脑部MRI能提供互补的软组织特征，有助于疾病筛查与诊断。然而，扫描时间受限、图像污染及不同成像协议常导致多对比图像不完整。当前方法虽在图像生成方面表现良好，但在合成关键肿瘤区域以及有效利用多对比脑部MRI上下文信息方面仍存在不足。为此，我们提出一种以生成为中心、分割辅助的闭环框架，结合检索增强生成。该方法采用生成对抗架构，仅用一个模型即可从任意可用对比图像组合中合成缺失对比图像。为显式捕捉肿瘤语义并聚焦于肿瘤区域的生成，增加一个辅助分割分支，预测肿瘤掩码并反馈至生成分支作为语义条件，从而在模型中学习到肿瘤感知表示，提升生成保真度。此外，提出双库检索增强策略：动态查询两个外部知识库——肿瘤掩码记忆库（用于关键肿瘤上下文）和跨图像对比特征记忆库（用于全局风格信息），以增强生成效果。在两个公开多对比磁共振脑部数据集（BraTs2020 和 UCSF-BMSR）上验证，该方法在医学脑部图像生成任务中表现优异，性能超越此前方法。代码已开源：https://github.com/iBizzard/SSCF.git。
原文摘要 · Abstract (English)
Multi-contrast brain MRI provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-contrast images. While current approaches excel in image synthesis, they often struggle to synthesize critical tumor regions and exploit contextual information in multi-contrast brain MRI effectively. To address this issue, we propose a synthesis-centric, segmentation-assisted closed-loop framework with retrieval augmentation synthesis. Our method overall takes a generative adversarial architecture, which aims to synthesize missing contrasts from any combination of available ones with a single model. To explicitly capture tumor semantics and focus synthesis on tumor regions, we add an auxiliary segmentation branch that predicts tumor masks and feeds them back as semantic conditioning in synthesis branch, thereby learning tumor-aware representations in the model and improving synthesis fidelity. Furthermore, we propose a dual-bank retrieval augmentation strategy. It dynamically queries two external knowledge bases, namely a tumor masks memory bank for crucial tumor context and cross-image contrast feature memory bank for global style information, to augment synthesis. Verified on two public multi-contrast magnetic resonance brain datasets: BraTs2020 and UCSF-BMSR, the proposed method is effective in handling medical brain images synthesis tasks and shows superior performance compared to previous methods. Code is available at:https://github.com/iBizzard/SSCF.git
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