针对肺结节数据稀缺，提出可控合成与增强框架提升小结节生成质量。
NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging
- 基于控制网的修正流生成器，确保解剖结构与病灶一致
- 在6个公开数据集上合成图像分布更接近真实，小结节检测率提升近一倍
- 特别适合医疗数据少时的结节检测与分类任务
尽管医学影像数据日益丰富，但肺癌筛查中关键的小肺结节等异常病灶仍因标注成本高而数据不足且标准不一。本文提出NodMAISI，一种解剖约束、结节导向的CT图像合成与增强框架，基于7,042名患者、8,841张CT、14,444个结节的多源统一队列训练。该框架整合三项技术：(i) 标准化数据清理与标注流程，为每张CT关联器官掩码和结节级标注；(ii) 基于MAISI-v2基础模块构建的控制网条件修正流生成器，保障解剖与病灶一致性；(iii) 病灶感知增强策略，通过受控缩小结节掩码同时保留周围解剖结构，生成配对的CT变体。在六个公开测试集上，相较MAISI-v2，NodMAISI显著提升分布保真度（真实到合成的FID：1.18~2.99 vs 1.69~5.21）。使用MONAI结节检测器分析显示，其平均敏感性大幅提高，更接近临床扫描（IMD-CT: 0.69 vs 0.39；DLCS24: 0.63 vs 0.20），尤其在<1厘米结节上改善显著。在下游结节恶性分类任务中，于LUNA25训练并外推至LUNA16、LNDbv4和DLCS24，NodMAISI在≤20%临床数据下提升AUC 0.07~0.11，10%数据下达0.12~0.21，有效缓解数据稀缺下的性能差距。
原文摘要 · Abstract (English)
Objective: Although medical imaging datasets are increasingly available, abnormal and annotation-intensive findings critical to lung cancer screening, particularly small pulmonary nodules, remain underrepresented and inconsistently curated. Methods: We introduce NodMAISI, an anatomically constrained, nodule-oriented CT synthesis and augmentation framework trained on a unified multi-source cohort (7,042 patients, 8,841 CTs, 14,444 nodules). The framework integrates: (i) a standardized curation and annotation pipeline linking each CT with organ masks and nodule-level annotations, (ii) a ControlNet-conditioned rectified-flow generator built on MAISI-v2's foundational blocks to enforce anatomy- and lesion-consistent synthesis, and (iii) lesion-aware augmentation that perturbs nodule masks (controlled shrinkage) while preserving surrounding anatomy to generate paired CT variants. Results: Across six public test datasets, NodMAISI improved distributional fidelity relative to MAISI-v2 (real-to-synthetic FID range 1.18 to 2.99 vs 1.69 to 5.21). In lesion detectability analysis using a MONAI nodule detector, NodMAISI substantially increased average sensitivity and more closely matched clinical scans (IMD-CT: 0.69 vs 0.39; DLCS24: 0.63 vs 0.20), with the largest gains for sub-centimeter nodules where MAISI-v2 frequently failed to reproduce the conditioned lesion. In downstream nodule-level malignancy classification trained on LUNA25 and externally evaluated on LUNA16, LNDbv4, and DLCS24, NodMAISI augmentation improved AUC by 0.07 to 0.11 at <=20% clinical data and by 0.12 to 0.21 at 10%, consistently narrowing the performance gap under data scarcity.
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