Automatic segmentation of amyloid plaques in MR images using unsupervised support vector machines.
Automatic segmentation of amyloid plaques in MR images using unsupervised support vector machines.
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DOI：
10.1002/mrm.23138
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发表时间：
2012-06
影响因子：
3.3
通讯作者：
Wyrwicz, Alice M.
中科院分区：
文献类型：
--
作者：
Iordanescu, Gheorghe;Venkatasubramanian, Palamadai N.;Wyrwicz, Alice M.
β-淀粉样多肽(A-β)沉积是阿尔茨海默病(AD)的重要病理标志。然而，可靠地量化人类和动物大脑中的淀粉样斑块仍然是一个挑战。本文提出了一种新的基于斑块固有磁共振信号特征的斑块自动分割算法。该算法通过分水岭变换来识别MR数据中的斑块候选区域，提取完全被高强度邻域包围的低强度区域。通过使用从MR数据强度得出的特征的无监督学习方法，这些候选对象被分类为斑块或非斑块。通过与组织学的比较，验证了该算法的性能。我们还展示了该算法在体外检测5×FAD APP转基因小鼠的斑块负荷与年龄相关的变化的能力。据我们所知，这项工作代表了第一种定量方法来表征MRI数据中的淀粉样斑块。该方法可以用来描述淀粉样蛋白沉积的时空进程，这对于了解AD模型小鼠斑块病理的演变以及在临床前试验中评估紧急淀粉样蛋白靶向治疗的有效性是必要的。
Deposition of the β-amyloid peptide (Aβ) is an important pathological hallmark of Alzheimer’s disease (AD). However, reliable quantification of amyloid plaques in both human and animal brains remains a challenge. We present here a novel automatic plaque segmentation algorithm based on the intrinsic MR signal characteristics of plaques. This algorithm identifies plaque candidates in MR data by using watershed transform, which extracts regions with low intensities completely surrounded by higher intensity neighbors. These candidates are classified as plaque or non-plaque by an unsupervised learning method using features derived from the MR data intensity. The algorithm performance is validated by comparison with histology. We also demonstrate the algorithm’s ability to detect age-related changes in plaque load ex vivo in 5×FAD APP transgenic mice. To our knowledge, this work represents the first quantitative method for characterizing amyloid plaques in MRI data. The proposed method can be used to describe the spatio-temporal progression of amyloid deposition, which is necessary for understanding the evolution of plaque pathology in mouse models of AD and to evaluate the efficacy of emergent amyloid-targeting therapies in preclinical trials.
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影响因子：
64.8
作者：
Meyer-Luehmann, Melanie;Spires-Jones, Tara L.;Hyman, Bradley T.
通讯作者：
Hyman, Bradley T.
影响因子：
11.2
作者：
Klunk, WE;Engler, H;Långström, B
通讯作者：
Långström, B
影响因子：
2.9
作者：
Huang, YL;Chen, DR
通讯作者：
Chen, DR
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
DOI：
10.1073/pnas.82.12.4245
发表时间：
1985-01-01
影响因子：
11.1
作者：
MASTERS, CL;SIMMS, G;BEYREUTHER, K
通讯作者：
BEYREUTHER, K