用流匹配技术高效生成边界真实的3D肿瘤掩码，提升医学影像合成效率与真实性。
TumorGen: Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching
- 用可变边界框替代二值掩码，灵活生成肿瘤区域。
- 仅需10步采样即完成合成，速度远超传统两阶段方法。
- 适合需要高真实感肿瘤数据的AI癌症诊断研究者使用。
肿瘤数据合成为解决标注医学数据稀缺问题提供了有效途径。然而，现有方法或因使用预定义掩码而限制肿瘤多样性，或采用计算成本高昂的两阶段流程，包含多轮去噪步骤，导致效率低下。此外，这些方法通常依赖二值掩码，无法捕捉肿瘤边界渐变的特性。我们提出TumorGen，一种基于修正流匹配的边界感知肿瘤掩码合成方法，包含三个关键组件：边界感知伪掩码生成模块，以灵活边界框替代严格二值掩码；空间约束向量场估计器，通过修正流匹配同步生成肿瘤潜在表示与掩码，确保计算效率；以及基于VAE的掩码优化器，增强边界真实感。TumorGen显著提升计算效率，仅需10次采样步骤即可完成合成，同时通过粗细粒度空间约束保持病理准确性。实验表明，TumorGen在效率与真实性上均优于现有肿瘤合成方法，为人工智能驱动的癌症诊断提供了重要支持。
原文摘要 · Abstract (English)
Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diversity by using predefined masks or employ computationally expensive two-stage processes with multiple denoising steps, causing computational inefficiency. Additionally, these methods typically rely on binary masks that fail to capture the gradual transitions characteristic of tumor boundaries. We present TumorGen, a novel Boundary-Aware Tumor-Mask Synthesis with Rectified Flow Matching for efficient 3D tumor synthesis with three key components: a Boundary-Aware Pseudo Mask Generation module that replaces strict binary masks with flexible bounding boxes; a Spatial-Constraint Vector Field Estimator that simultaneously synthesizes tumor latents and masks using rectified flow matching to ensure computational efficiency; and a VAE-guided mask refiner that enhances boundary realism. TumorGen significantly improves computational efficiency by requiring fewer sampling steps while maintaining pathological accuracy through coarse and fine-grained spatial constraints. Experimental results demonstrate TumorGen's superior performance over existing tumor synthesis methods in both efficiency and realism, offering a valuable contribution to AI-driven cancer diagnostics.
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