Right Ventricle Segmentation Using a 3D Cylindrical Shape Model

Oliver Moolan-Feroze, Majid Mirmehdi, Mark C K Hamilton

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)

Abstract

Accurate segmentation of the right ventricle is a necessary precursor for the assessment of cardiac function. However, the large shape variations exhibited by the right ventricle make automated segmentation a difficult problem. In this work, we explore the ability of a cylindrical shape model to compactly represent and accurately segment this wide range of morphologies. The novelty of this method lies in the design of the fitting function which incorporates learned shape information into a Markov Random Field formulation. Furthermore, the shape model is integrated with a 2D image-based segmentation method, further refining the accuracy of the extracted regions. To evaluate our method, we applied it to the independently evaluated MICCAI RV Segmentation Challenge dataset. Our method performed as well as, or better than, the state-of-the-art methods, validating its suitability for this difficult application.
Original languageEnglish
Title of host publication2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)
Subtitle of host publicationProceedings of a meeting held 13-16 April 2016, Prague, Czech Republic
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages44-48
Number of pages5
ISBN (Electronic)9781479923496
ISBN (Print)9781479923519
DOIs
Publication statusPublished - Jul 2016

Publication series

NameProceedings of the IEEE International Symposium on Biomedical Imaging (ISBI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1945-8452

Keywords

  • shape model
  • Right Ventricle Segmentation
  • Markov Random Fields
  • MRI

Fingerprint Dive into the research topics of 'Right Ventricle Segmentation Using a 3D Cylindrical Shape Model'. Together they form a unique fingerprint.

Cite this