Fully automated segmentation of long-axis mri strain-encoded (SENC) images using active shape model (ASM)

Ahmed A. Harouni, David A. Bluemke, Nael F. Osman

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

Abstract

Myocardial strain is an important measure used for assessing regional function, which could help in detecting myocardial infarction as well as following up with patients with heart diseases. MRI Strain-Encoding technique (SENC) produces strain values throughout the cardiac cycle. SENC has proved to be one of the few techniques that can quantify right ventricle (RV) regional function. However, SENC images suffer from low signal-to-noise ratio (SNR). In this paper we present a fully automatic method to detect, segment, and track the myocardium throughout the cardiac cycle using prior knowledge of the shape of the 4-champers long-axis (LA) view. Our detection algorithm has a success rate of 91% (33/36 cases). The dice similarity coefficient was 0.81 ± 0.07 and 0.71 ± 0.15 for the left ventricle-septum (LV-SEP) and RV respectively, yielding a high correlation R = 0.91 between strain values measured from automatic and manual segmentation.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
Pages827-830
Number of pages4
DOIs
StatePublished - Nov 17 2009
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Other

Other2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Country/TerritoryUnited States
CityBoston, MA
Period6/28/097/1/09

Keywords

  • Active shape model
  • Image segmentation
  • Right ventricle strain
  • Strain encoded MRI

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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