Left ventricular segmentation challenge from cardiac MRI: a collation study

Authors:
Avan Suinesiaputra;Brett R. Cowan;J. Paul Finn;Carissa G. Fonseca;Alan H. Kadish;Daniel C. Lee;Pau Medrano-Gracia;Simon K. Warfield;Wenchao Tao;Alistair A. Young
Affiliations:
Auckland Bioengineering Institute, University of Auckland, New Zealand;Auckland Bioengineering Institute, University of Auckland, New Zealand;Department of Radiological Sciences, University of California, Los Angeles;Department of Radiological Sciences, University of California, Los Angeles;Division of Cardiology, Northwestern University;Division of Cardiology, Northwestern University;Auckland Bioengineering Institute, University of Auckland, New Zealand;Computational Radiology Laboratory, Harvard Medical School;Department of Radiological Sciences, University of California, Los Angeles;Auckland Bioengineering Institute, University of Auckland, New Zealand
Venue:
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Year:
2011

Citing 4
Cited 1

Bioinformatics
Automatic segmentation of the myocardium in cine MR images using deformable registration

STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Layered spatio-temporal forests for left ventricle segmentation from 4d cardiac MRI data

STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Myocardial segmentation using contour-constrained optical flow tracking

STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges

Large scale left ventricular shape atlas using automated model fitting to contours

FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents collated results from the left ventricular (LV) cardiac MRI segmentation challenge as part of STACOM'11. Clinical cases from patients with myocardial infarction (100 test and 100 validation cases) were randomly selected from the Cardiac Atlas Project (CAP) database. Two independent sets of expert (manual) segmentation from different sources that are available from the CAP database were included in this study. Automated segmentations from five groups were contributed in the challenge. The total number of cases with segmentations from all seven raters was 18. For these cases, a ground truth "consensus" segmentation was estimated based on all raters using an Expectation-Maximization (EM) method (the STAPLE algorithm).