Shi Yonggang, Tu Zhuowen, Reiss Allan L, Dutton Rebecca A, Lee Agatha D, Galaburda Albert M, Dinov Ivo, Thompson Paul M, Toga Arthur W
Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
Inf Process Med Imaging. 2007;20:98-109. doi: 10.1007/978-3-540-73273-0_9.
In this paper we propose an automated approach for joint sulci detection on cortical surfaces by using graphical models and boosting techniques to incorporate shape priors of major sulci and their Markovian relations. For each sulcus, we represent it as a node in the graphical model and associate it with a sample space of candidate curves, which is generated automatically using the Hamilton-Jacobi skeleton of sulcal regions. To take into account individual as well as joint priors about the shape of major sulci, we learn the potential functions of the graphical model using AdaBoost algorithm to select and fuse information from a large set of features. This discriminative approach is especially powerful in capturing the neighboring relations between sulcal lines, which are otherwise hard to be captured by generative models. Using belief propagation, efficient inferencing is then performed on the graphical model to estimate each sulcus as the maximizer of its final belief. On a data set of 40 cortical surfaces, we demonstrate the advantage of joint detection on four major sulci: central, precentral, postcentral and the sylvian fissure.
在本文中，我们提出了一种用于在皮质表面进行联合脑沟检测的自动化方法，该方法使用图形模型和增强技术来纳入主要脑沟的形状先验及其马尔可夫关系。对于每个脑沟，我们将其表示为图形模型中的一个节点，并将其与候选曲线的样本空间相关联，该样本空间是使用脑沟区域的哈密顿 - 雅可比骨架自动生成的。为了考虑关于主要脑沟形状的个体以及联合先验，我们使用AdaBoost算法学习图形模型的势函数，以从大量特征中选择并融合信息。这种判别式方法在捕捉脑沟线之间的相邻关系方面特别强大，而生成模型很难捕捉到这些关系。然后，使用信念传播在图形模型上进行高效推理，以将每个脑沟估计为其最终信念的最大化者。在一个包含40个皮质表面的数据集上，我们展示了在四个主要脑沟（中央沟、中央前沟、中央后沟和外侧裂）上进行联合检测的优势。
