Karimaghaloo Zahra, Arnold Douglas L, Collins D Louis, Arbel Tal
Centre for Intelligent Machines, McGill University, Canada.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):379-86. doi: 10.1007/978-3-642-33418-4_47.
The detection of gad-enhancing lesions in brain MRI of Multiple Sclerosis (MS) patients is of great interest since they are important markers of disease activity. However, many of the enhancing voxels are associated with normal structures (i.e. blood vessels) or noise in the MRI, making the detection of gad-enhancing lesions a challenging task. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic Hierarchical Conditional Random Field (HCRF) framework for detection of gad-enhancing lesions in brain images of patients with MS. In the first level, a CRF with unary and pairwise potentials is used to identify candidate lesion voxel. In the second level, these lesion candidates are grouped based on anatomical and spatial features, and feature-specific lesion based CRF models are designed for each group. This lesion level CRF incorporates higher order potentials which account for shape, group intensities and symmetries. The proposed algorithm is trained on 92 multimodal clinical datasets acquired from Relapsing-Remitting MS patients during multicenter clinical trials and is evaluated on 30 independent cases. The experimental results show a sensitivity of 98%, a positive predictive value of 66% and an average false positive count of 1.55, outperforming the CRF and MRF frameworks proposed in.
多发性硬化症（MS）患者脑部磁共振成像（MRI）中钆增强病变的检测备受关注，因为它们是疾病活动的重要标志物。然而，许多增强体素与正常结构（即血管）或MRI噪声相关，这使得钆增强病变的检测成为一项具有挑战性的任务。此外，这些病变通常较小且靠近血管。在本文中，我们提出了一种自动的概率分层条件随机场（HCRF）框架，用于检测MS患者脑部图像中的钆增强病变。在第一级，使用具有一元和成对势的CRF来识别候选病变体素。在第二级，根据解剖和空间特征对这些病变候选者进行分组，并为每个组设计基于特征的特定病变CRF模型。这种病变级CRF纳入了高阶势，其考虑了形状、组强度和对称性。所提出的算法在多中心临床试验期间从复发缓解型MS患者获取的92个多模态临床数据集上进行训练，并在30个独立病例上进行评估。实验结果显示灵敏度为98%，阳性预测值为66%，平均假阳性计数为1.55，优于文中提出的CRF和MRF框架。
