不确定性作为扩散模型医学图像合成中语义正确性的代理指标

中文摘要 AI 辅助

扩散模型可以从非增强CT（NCCT）合成对比增强CT（CECT），从而避免对比剂的使用及其环境和患者获取成本。然而，视觉上逼真的图像不一定在解剖学上是正确的，用于评估生成质量的像素强度和特征空间相似性指标并不能直接衡量解剖学正确性。在本研究中，我们探讨不确定性是否可以作为扩散模型医学图像合成中语义正确性的代理指标。我们使用AortaDiff（一种多任务扩散框架，可同时生成CECT图像和管腔分割）研究NCCT到CECT的合成。分割输出提供了生成血管解剖结构的显式表示，使得源自分割的误差能够用作生成正确性的定量度量。我们比较了六种方法，涵盖权重不确定性（Ensemble、HyperDiff、BayesDiff）、架构扰动（MCDropout）、生成随机性（RDS）和输入扰动（TTA），在像素、区域和图像级别以及临床相关的分布外（OOD）病例检测方面进行了比较。不确定性在所有三个空间尺度上均具有信息量，在分布偏移下的外部多中心数据集上仍然具有信息量，并支持OOD检测。MCDropout在六种方法中脱颖而出：它在每个尺度上都位列领先方法，在外部数据集上泛化良好，并且可以在推理时对任何已经使用dropout训练的模型启用，因此可靠的不确定性无需额外训练成本。不确定性可靠地标记严重失败，但在已经高质量的图像中区分能力较差。这些发现支持不确定性作为NCCT到CECT合成中质量过滤、可靠性评估和OOD检测的实用且计算经济的信号。

英文摘要

Diffusion models can synthesise contrast-enhanced CT (CECT) from non-contrast CT (NCCT), avoiding contrast administration and its environmental and patient-access costs. However, visually realistic images are not necessarily anatomically correct, and the pixel-intensity and feature-space similarity metrics used to assess generation quality do not directly measure anatomical correctness. In this work, we investigate whether uncertainty can serve as a proxy for semantic correctness in diffusion-based medical image synthesis. We study NCCT-to-CECT synthesis using AortaDiff, a multitask diffusion framework that jointly generates CECT images and lumen segmentations. The segmentation output provides an explicit representation of the generated vascular anatomy, enabling segmentation-derived errors to be used as a quantitative measure of generation correctness. Six methods spanning weight (Ensemble, HyperDiff, BayesDiff), architecture-perturbation (MCDropout), generative-stochasticity (RDS) and input-perturbation (TTA) uncertainty are compared at the pixel, region and image levels, and for detection of clinically relevant out-of-distribution (OOD) cases. Uncertainty proves informative at all three spatial scales, remains informative on an external multi-centre dataset under distribution shift, and supports OOD detection. MCDropout stands out among the six: it ranks among the leading methods at every scale, generalizes well on the external dataset, and can be enabled at inference on any model already trained with dropout, so reliable uncertainty comes at no extra training cost. Uncertainty reliably flags severe failures but discriminates poorly among already high-quality images. These findings support uncertainty as a practical and computationally economical signal for quality filtering, reliability assessment and OOD detection in NCCT-to CECT synthesis.

发表机构

• University of Oxford（牛津大学）

机构由 AI 辅助整理，请以论文原文为准。