为脑白质高信号分割引入不确定性量化，识别模型‘沉默失败’并提升临床评分准确性
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification
- 采用深度集成与随机分割网络进行不确定性建模，捕捉分割边界模糊区域
- 结合不确定性信息使Fazekas评分分类准确率提升至0.74（深部白质）
- 可有效检测低质量影像中的分割异常，适合临床辅助诊断场景
脑白质高信号（WMH）是小血管病的重要影像标志，在研究与临床中具有重要意义。但由于其形态、位置、大小高度变异，边界模糊，且与缺血灶和运动伪影强度相似，分割极具挑战。本文评估了多种不确定性量化（UQ）技术在不同测试数据分布下的表现。结果表明，UQ可识别出模型漏分的深部白质小病灶，即‘沉默失败’。结合随机分割网络与深度集成的方法在Dice分数与绝对体积差异百分比（AVD）上均最优，并能揭示WMH与缺血灶之间的歧义区域。进一步提出一种新方法，利用体素级WMH概率及UQ图的空间特征进行临床Fazekas评分分类。结果显示，加入不确定性信息后，深部白质区平衡准确率达0.74（根布里尔得分0.65），侧脑室周围区达0.74（0.64）。同时，具备高采样多样性的随机UQ技术显著提升了对低质量分割的检测能力。
原文摘要 · Abstract (English)
White Matter Hyperintensities (WMH) are key neuroradiological markers of small vessel disease present in brain MRI. Assessment of WMH is important in research and clinics. However, WMH are challenging to segment due to their high variability in shape, location, size, poorly defined borders, and similar intensity profile to other pathologies (e.g stroke lesions) and artefacts (e.g head motion). In this work, we assess the utility and semantic properties of the most effective techniques for uncertainty quantification (UQ) in segmentation for the WMH segmentation task across multiple test-time data distributions. We find UQ techniques reduce 'silent failure' by identifying in UQ maps small WMH clusters in the deep white matter that are unsegmented by the model. A combination of Stochastic Segmentation Networks with Deep Ensembles also yields the highest Dice and lowest Absolute Volume Difference % (AVD) score and can highlight areas where there is ambiguity between WMH and stroke lesions. We further demonstrate the downstream utility of UQ, proposing a novel method for classification of the clinical Fazekas score using spatial features extracted from voxelwise WMH probability and UQ maps. We show that incorporating WMH uncertainty information improves Fazekas classification performance and calibration. Our model with (UQ and spatial WMH features)/(spatial WMH features)/(WMH volume only) achieves a balanced accuracy score of 0.74/0.67/0.62, and root brier score of 0.65/0.72/0.74 in the Deep WMH and balanced accuracy of 0.74/0.73/0.71 and root brier score of 0.64/0.66/0.68 in the Periventricular region. We further demonstrate that stochastic UQ techniques with high sample diversity can improve the detection of poor quality segmentations.
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