提出新指标WASABI，精准评估合成脑MRI的解剖真实性。
WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs
- 用深度学习分割脑区体积，再以多变量水氏距离比对真实与合成图像的解剖分布。
- 在五种生成模型上验证，即使视觉逼真度极高，仍能检测出解剖差异。
- 适合关注医学影像真实性的研究者和临床应用开发者使用。
生成模型通过数据增强、质量提升和罕见病研究推动神经影像发展。尽管合成MRI在视觉真实感上取得进展，现有评估仍聚焦纹理与感知，缺乏对关键解剖保真度的敏感性。本文提出新指标WASABI（基于水氏距离的解剖脑指数），利用基于深度学习的脑分割工具SynthSeg提取每幅MRI的脑区体积，并采用多变量水氏距离比较真实与合成解剖结构的分布差异。在两个真实数据集及五个生成模型的合成MRI上进行受控实验，结果表明WASABI在量化解剖偏差方面显著优于传统图像级指标，即便合成图像达到近乎完美的视觉质量。研究呼吁从视觉检查和常规指标转向以解剖保真度为核心评价标准，以实现临床有意义的脑MRI生成。代码已公开于https://github.com/BahramJafrasteh/wasabi-mri。
原文摘要 · Abstract (English)
Generative models enhance neuroimaging through data augmentation, quality improvement, and rare condition studies. Despite advances in realistic synthetic MRIs, evaluations focus on texture and perception, lacking sensitivity to crucial anatomical fidelity. This study proposes a new metric, called WASABI (Wasserstein-Based Anatomical Brain Index), to assess the anatomical realism of synthetic brain MRIs. WASABI leverages \textit{SynthSeg}, a deep learning-based brain parcellation tool, to derive volumetric measures of brain regions in each MRI and uses the multivariate Wasserstein distance to compare distributions between real and synthetic anatomies. Based on controlled experiments on two real datasets and synthetic MRIs from five generative models, WASABI demonstrates higher sensitivity in quantifying anatomical discrepancies compared to traditional image-level metrics, even when synthetic images achieve near-perfect visual quality. Our findings advocate for shifting the evaluation paradigm beyond visual inspection and conventional metrics, emphasizing anatomical fidelity as a crucial benchmark for clinically meaningful brain MRI synthesis. Our code is available at https://github.com/BahramJafrasteh/wasabi-mri.
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