Automatic segmentation of amyloid plaques in MR images using unsupervised support vector machines

Abstract

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 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.