Conical deformable model for myocardial segmentation in late-enhanced MRI

Xènia Albá*, Rosa M.Figueras I Ventura, Karim Lekadir, Alejandro F. Frangi

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a conical 3D deformable template for fully automatic and robust segmentation of late-enhanced MRI (LE-MRI) datasets. The proposed technique has several advantages over existing works. Firstly, it uses a thick-walled conical model that is suitable to derive fully automatic and reliable initialization by taking into account potential short-axis misalignments. Furthermore, it uses to its advantage the geometrical and appearance properties of the blood pool to decouple the endocardial and epicardial segmentations. The final epicardial result is obtained using thickness smoothness measures constrained on the initial robust segmentation of the endocardium. Detailed validation using 20 LE-MRI datasets and comparison to previous work demonstrates that the technique is reliable and promising for clinical assessment of LE-MRI data.

Original languageEnglish
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages270-273
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: 2 May 20125 May 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/TerritorySpain
CityBarcelona
Period2/05/125/05/12

Keywords

  • 3D model
  • deformable model
  • LE-MRI
  • Myocardial segmentation
  • slice alignment correction

Fingerprint

Dive into the research topics of 'Conical deformable model for myocardial segmentation in late-enhanced MRI'. Together they form a unique fingerprint.

Cite this