Paper Info
Title
Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning.
Abstract
Changes in brain morphology and white matter lesions are two hallmarks of multiple sclerosis (MS) pathology, but their variability beyond volumetrics is poorly characterized. To further our understanding of complex MS pathology, we aim to build a statistical model of brain images that can automatically discover spatial patterns of variability in brain morphology and lesion distribution. We propose building such a model using a deep belief network (DBN), a layered network whose parameters can be learned from training images. In contrast to other manifold learning algorithms, the DBN approach does not require a prebuilt proximity graph, which is particularly advantageous for modeling lesions, because their sparse and random nature makes defining a suitable distance measure between lesion images challenging. Our model consists of a morphology DBN, a lesion DBN, and a joint DBN that models concurring morphological and lesion patterns. Our results show that this model can automatically discover the classic patterns of MS pathology, as well as more subtle ones, and that the parameters computed have strong relationships to MS clinical scores.
Year
DOI
Venue
2014
10.1007/978-3-319-10470-6_58
Lecture Notes in Computer Science
Keywords
Field
DocType
Population modeling,multiple sclerosis,T2 lesion,machine learning,brain imaging,MRI,deep learning,deep belief networks
Lesion,Pattern recognition,Computer science,Deep belief network,Brain morphometry,Artificial intelligence,Statistical model,Neuroimaging,Deep learning,Nonlinear dimensionality reduction,Hyperintensity
Conference
Volume
Issue
ISSN
8674
Pt 2
0302-9743
Citations 
PageRank 
References 
12
0.65
10
Authors
5
Name
Order
Citations
PageRank
Brosch Tom11708.29
Youngjin Yoo21229.07
David K. B. Li31318.27
Anthony Traboulsee41175.82
Roger C. Tam524416.61