Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada.
IEEE Trans Med Imaging. 2012 Jun;31(6):1181-94. doi: 10.1109/TMI.2012.2186639. Epub 2012 Feb 3.
Gadolinium-enhancing lesions in brain magnetic resonance imaging of multiple sclerosis (MS) patients are of great interest since they are markers of disease activity. Identification of gadolinium-enhancing lesions is particularly challenging because the vast majority of enhancing voxels are associated with normal structures, particularly blood vessels. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic framework for segmentation of gadolinium-enhancing lesions in MS using conditional random fields. Our approach, through the integration of different components, encodes different information such as correspondence between the intensities and tissue labels, patterns in the labels, or patterns in the intensities. The proposed algorithm is evaluated on 80 multimodal clinical datasets acquired from relapsing-remitting MS patients in the context of multicenter clinical trials. The experimental results exhibit a sensitivity of 98% with a low false positive lesion count. The performance of the proposed algorithm is also compared to a logistic regression classifier, a support vector machine and a Markov random field approach. The results demonstrate superior performance of the proposed algorithm at successfully detecting all of the gadolinium-enhancing lesions while maintaining a low false positive lesion count.
脑磁共振成像（MRI）中多发性硬化症（MS）患者的钆增强病灶是非常重要的，因为它们是疾病活动的标志物。识别钆增强病灶具有挑战性，因为绝大多数增强体素与正常结构，特别是血管有关。此外，这些病灶通常较小且靠近血管。在本文中，我们提出了一种使用条件随机场（CRF）对 MS 中的钆增强病灶进行自动、概率分割的方法。我们的方法通过整合不同的组件，对不同的信息进行编码，如强度和组织标签之间的对应关系、标签中的模式或强度中的模式。所提出的算法在 80 个多模态临床数据集上进行了评估，这些数据集是在多发性硬化症临床试验的背景下从复发缓解型 MS 患者中获得的。实验结果显示，该算法的灵敏度为 98%，假阳性病灶数较低。还将所提出的算法的性能与逻辑回归分类器、支持向量机和马尔可夫随机场方法进行了比较。结果表明，所提出的算法在成功检测所有钆增强病灶的同时保持较低的假阳性病灶数方面表现出了优越的性能。
