用弗雷歇距离损失改进医学图像生成模型

中文摘要 AI 辅助

扩散生成模型在合成医学图像生成方面潜力巨大，但难以捕捉边界不规则的异质性肿瘤的复杂形态特征，限制了其在下游临床任务中的应用。这源于标准去噪目标会平滑肿瘤特征的高方差不规则结构。为此，我们提出用弗雷歇距离损失（FD-loss）微调这些生成模型。它能在预训练编码器空间中对齐真实图像和生成图像的一阶和二阶特征统计量，促使生成器捕捉异质性肿瘤的复杂结构变化。我们在多个数据集上集成FD-loss，用其正则化的合成数据训练下游分割网络，性能显著提升，肿瘤DSC比仅使用未正则化合成增强提高>5%。定性分析表明这些改进与更忠实的肿瘤合成和更少的分割幻觉有关。结果表明FD-loss是改进医学图像生成模型临床工作流程的有效正则化方法。

英文摘要

Diffusion generative models have demonstrated immense potential for synthetic medical image generation. However, these models often struggle to capture complex morphological characteristics of heterogeneous tumors with irregular boundaries, limiting their utility for downstream clinical tasks such as segmentation. This limitation stems from the standard denoising objective: minimizing a per-pixel error, which smooths high-variance irregular structures characteristic of tumors. To address this, we propose finetuning these generative models with Fréchet Distance loss (FD-loss). FD-loss aligns the first and second order feature statistics of real and generated images in a pretrained encoder space, encouraging the generator to capture complex structural variations characteristic of heterogeneous tumors. We integrate FD-loss across diverse architectural settings, using both natural- and medical-image encoders on multiple liver and brain cancer datasets spanning CT and MRI modalities. Downstream segmentation networks trained on our FD-regularized synthetic data consistently achieve superior performance, improving tumor DSC by $>$$5\%$ over unregularized synthetic augmentation alone. Qualitative analysis suggests these gains are associated with more faithful tumor synthesis and fewer segmentation hallucinations. Our results show FD-loss as an effective regularizer for medical image generative models to improve clinical workflows.

发表机构

• Yale University（耶鲁大学）

机构由 AI 辅助整理，请以论文原文为准。