Paper Info
Title
A fast convex optimization approach to segmenting 3D scar tissue from delayed-enhancement cardiac MR images.
Abstract
We propose a novel multi-region segmentation approach through a partially-ordered ports (POP) model to segment myocardial scar tissue solely from 3D cardiac delayed-enhancement MR images (DE-MRI). The algorithm makes use of prior knowledge of anatomical spatial consistency and employs customized label ordering to constrain the segmentation without prior knowledge of geometric representation. The proposed method eliminates the need for regional constraint segmentations, thus reduces processing time and potential sources of error. We solve the proposed optimization problem by means of convex relaxation and introduce its duality: the hierarchical continuous max-flow (HMF) model, which amounts to an efficient numerical solver to the resulting convex optimization problem. Experiments are performed over ten DE-MRI data sets. The results are compared to a FWHM (full-width at half-maximum) method and the inter- and intra-operator variabilities assessed.
Year
DOI
Venue
2012
10.1007/978-3-642-33415-3_81
MICCAI
Keywords
Field
DocType
cardiac delayed-enhancement mr image,anatomical spatial consistency,de-mri data set,convex optimization problem,novel multi-region segmentation approach,prior knowledge,scar tissue,fast convex optimization approach,proposed optimization problem,convex relaxation,regional constraint segmentation,delayed-enhancement cardiac mr image
Computer vision,Data set,Market segmentation,Pattern recognition,Computer science,Segmentation,Image segmentation,Duality (optimization),Artificial intelligence,Solver,Optimization problem,Convex optimization
Conference
Volume
Issue
ISSN
15
Pt 1
0302-9743
Citations 
PageRank 
References 
10
0.68
6
Authors
8
Name
Order
Citations
PageRank
Martin Rajchl142134.67
Jing Yuan218212.30
James A. White3527.70
Cyrus Nambakhsh4985.10
Eranga Ukwatta515418.10
Feng Li6424.24
John Stirrat7372.42
Terry M. Peters81335181.71