通过改进算法精准识别多发性硬化症脑部病灶，提升病情评估准确性。
Unique MS Lesion Identification from MRI
- 结合海森矩阵与随机游走算法，从概率图中分离每个独特病灶。
- 可检测到传统方法遗漏的病灶，且准确分割融合病灶。
- 适用于临床研究中更精确地统计病灶体积与数量，辅助病情判断。
多发性硬化症（MS）白质病变（WMLs）的准确定位对评估疾病进展至关重要。尽管常规磁共振成像（MRI）可识别病变，但总病变负荷与扩展残疾状态量表（EDSS）相关性较差；而平均独特病灶体积已被证明与EDSS相关。本研究在前人基础上，通过病灶概率图计算海森矩阵，并应用随机游走算法估算每个独特病灶的体积。合成图像验证了该方法能准确计数病灶数量。结果表明：1）可完整识别所有病灶，包括以往方法遗漏者；2）能更好分离融合病灶；3）能准确捕获给定概率图中的总白质病变体积。该方法将使基于脑部MRI的病灶统计更具临床意义。
原文摘要 · Abstract (English)
Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS; whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods; 2) we can better separate confluent lesions; and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful statistics to be computed from WMLs in brain MRIs
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