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Infarct segmentation challenge on delayed enhancement MRI of the left ventricle

Research output: Chapter in Book/Report/Conference proceedingConference paper

Rashed Karim, Piet Claus, Zhong Chen, R. James Housden, Samantha Obom, Harminder Gill, YingLiang Ma, Prince Acheampong, Mark O'Neill, Reza Razavi, Tobias Schaeffter, Kawal S. Rhode

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Subtitle of host publicationBook Subtitle Third International Workshop, STACOM 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers
EditorsOscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
PublisherSpringer Berlin Heidelberg
Pages97-104
Number of pages8
Volume7746 LNCS
ISBN (Electronic)978-3-642-36961-2
ISBN (Print)978-3-642-36960-5
DOIs
Publication statusPublished - 2013
Event3rd International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2012 - Nice, France
Duration: 5 Oct 20125 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7746 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference3rd International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2012
CountryFrance
CityNice
Period5/10/20125/10/2012

King's Authors

Abstract

This paper presents collated results from the Delayed Enhancement MRI (DE-MRI) segmentation challenge at MICCAI 2012. DE-MRI Images from fifteen patients and fifteen pigs were randomly selected from two different imaging centres. Three independent sets of manual segmentations were obtained for each image and included in this study. A ground truth consensus segmentation based on all human rater segmentations was obtained using an Expectation-Maximization (EM) method (the STAPLE method). Automated segmentations from five groups contributed to this challenge.

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