Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR.
Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR.
复制标题
DOI：
10.1007/978-3-642-02498-6_20
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Carmichael, Owen
中科院分区：
文献类型：
--
作者：
Schwarz, Christopher;Fletcher, Evan;DeCarli, Charles;Carmichael, Owen
本文提出了一种基于运行时PD-， T1-和T2-加权脑结构磁共振（MR）图像以及标记训练示例的脑白质高强度（WMH）检测方法。与大多数先前的方法不同，该方法能够在没有液体衰减（FLAIR）图像的情况下可靠地检测老年人大脑中的wmh。它的成功是由于从基于flair的WMH检测的真值示例中学习了WMH空间分布的概率模型和邻域依赖关系。这些模型与马尔可夫随机场（MRF）框架中wmh的PD、T1和T2强度的概率模型相结合，该框架提供了在新的测试图像中推断wmh位置的机制。该方法被证明可以准确地检测来自学术痴呆诊所的114名老年受试者的wmh。实验表明，标准的现成的MRF训练和推理方法提供了稳健的结果，并且增加邻域依赖模型的复杂性不一定有助于性能。当训练和测试数据来自不同的扫描仪和受试者池时，该方法也表现良好。
This paper presents a method for detection of cerebral white matter hyperintensities (WMH) based on run-time PD-, T1-, and T2- weighted structural magnetic resonance (MR) images of the brain along with labeled training examples. Unlike most prior approaches, the method is able to reliably detect WMHs in elderly brains in the absence of fluid-attenuated (FLAIR) images. Its success is due to the learning of probabilistic models of WMH spatial distribution and neighborhood dependencies from ground-truth examples of FLAIR-based WMH detections. These models are combined with a probabilistic model of the PD, T1, and T2 intensities of WMHs in a Markov Random Field (MRF) framework that provides the machinery for inferring the positions of WMHs in novel test images. The method is shown to accurately detect WMHs in a set of 114 elderly subjects from an academic dementia clinic. Experiments show that standard off-the-shelf MRF training and inference methods provide robust results, and that increasing the complexity of neighborhood dependency models does not necessarily help performance. The method is also shown to perform well when training and test data are drawn from distinct scanners and subject pools.
登录
查看更多内容
影响因子：
--
作者：
Taylor, WD;Steffens, DC;Krishnan, KRR
通讯作者：
Krishnan, KRR
影响因子：
4.5
作者：
Torr, PHS;Zisserman, A
通讯作者：
Zisserman, A
影响因子：
1.8
作者：
JIROUSEK, R;PREUCIL, S
通讯作者：
PREUCIL, S
影响因子：
9.9
作者：
Yoshita, M.;Fletcher, E.;DeCarli, C. S.
通讯作者：
DeCarli, C. S.
影响因子：
5.7
作者：
Admiraal-Behloul, F;van den Heuvel, DMJ;Relber, JHC
通讯作者：
Relber, JHC