Title
Characterizing pathological deviations from normality using constrained manifold-learning.
Abstract
We propose a technique to represent a pathological pattern as a deviation from normality along a manifold structure. Each subject is represented by a map of local motion abnormalities, obtained from a statistical atlas of motion built from a healthy population. The algorithm learns a manifold from a set of patients with varying degrees of the same pathology. The approach extends recent manifold-learning techniques by constraining the manifold to pass by a physiologically meaningful origin representing a normal motion pattern. Individuals are compared to the manifold population through a distance that combines a mapping to the manifold and the path along the manifold to reach its origin. The method is applied in the context of cardiac resynchronization therapy (CRT), focusing on a specific motion pattern of intra-ventricular dyssynchrony called septal flash (SF). We estimate the manifold from 50 CRT candidates with SF and test it on 38 CRT candidates and 21 healthy volunteers. Experiments highlight the need of nonlinear techniques to learn the studied data, and the relevance of the computed distance for comparing individuals to a specific pathological pattern.
Year
DOI
Venue
2011
10.1007/978-3-642-23626-6_32
MICCAI (3)
Keywords
Field
DocType
computed distance,manifold structure,specific pathological pattern,pathological deviation,crt candidate,pathological pattern,specific motion pattern,local motion abnormality,healthy population,normal motion pattern,manifold population
Manifold structure,Normality,Computer vision,Population,Nonlinear system,Pattern recognition,Computer science,Kernel principal component analysis,Artificial intelligence,Nonlinear dimensionality reduction,Cardiac resynchronization therapy,Manifold
Conference
Volume
Issue
ISSN
14
Pt 3
0302-9743
Citations 
PageRank 
References 
4
0.61
9
Authors
4
Name
Order
Citations
PageRank
N Duchateau119920.53
M De Craene241141.85
G Piella336643.86
Alejandro F. Frangi44333309.21