用无监督方法检测中风后脑部异常，效果优于传统方法。
Unsupervised Detection of Post-Stroke Brain Abnormalities
- 基于流模型构建无监督异常检测框架
- 在ATLAS数据上，病变分割Dice达0.37，非病灶异常检出率提升至0.62
- 用健康人数据训练更有效，适合临床影像筛查
中风后MRI不仅能显示局灶性病灶，还能揭示萎缩、脑室扩大等继发性结构改变。这些异常正被视作康复与预后的影像生物标志物，但现有监督分割方法难以捕捉。我们评估了REFLECT这一基于流的生成模型，在无监督条件下检测中风患者中的局灶性和非局灶性异常的能力。基于ATLAS数据集的双专家中心切片标注，采用自由响应ROC分析评估异常图性能。分别使用中风患者无病灶切片（ATLAS）和健康对照（IXI）数据训练两个模型。在ATLAS测试集上，基于IXI数据训练的模型取得更高病变分割效果（Dice = 0.37 vs 0.27），并显著提升对非病灶异常的敏感度（FROC = 0.62 vs 0.43）。使用健康解剖结构训练能更好建模正常变异，实现更广泛、可靠的结构异常检测。
原文摘要 · Abstract (English)
Post-stroke MRI not only delineates focal lesions but also reveals secondary structural changes, such as atrophy and ventricular enlargement. These abnormalities, increasingly recognised as imaging biomarkers of recovery and outcome, remain poorly captured by supervised segmentation methods. We evaluate REFLECT, a flow-based generative model, for unsupervised detection of both focal and non-lesional abnormalities in post-stroke patients. Using dual-expert central-slice annotations on ATLAS data, performance was assessed at the object level with Free-Response ROC analysis for anomaly maps. Two models were trained on lesion-free slices from stroke patients (ATLAS) and on healthy controls (IXI) to test the effect of training data. On ATLAS test subjects, the IXI-trained model achieved higher lesion segmentation (Dice = 0.37 vs 0.27) and improved sensitivity to non-lesional abnormalities (FROC = 0.62 vs 0.43). Training on fully healthy anatomy improves the modelling of normal variability, enabling broader and more reliable detection of structural abnormalities.
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