期刊
MAGNETIC RESONANCE IN MEDICINE
卷 67, 期 6, 页码 1794-1802出版社
WILEY
DOI: 10.1002/mrm.23138
关键词
amyloid plaques; segmentation; MR imaging; transgenic mice; catchment basins; classification; validation
资金
- National Institutes of Health [RO1 AG 027424-01A2, S10 RR13880]
Deposition of the beta-amyloid peptide (A beta) 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 nonplaque 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 amyloid precursor protein (APP) transgenic mice that coexpress five familial AD mutations (5xFAD mice). To our knowledge, this study represents the first quantitative method for characterizing amyloid plaques in MRI data. The proposed method can be used to describe the spatiotemporal progression of amyloid deposition, which is necessary for understanding the evolution of plaque pathology in mouse models of Alzheimer's disease and to evaluate the efficacy of emergent amyloid-targeting therapies in preclinical trials. Magn Reson Med, 2011. (c) 2011 Wiley-Liss, Inc.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据