International Conference on Medical Image Computing and Computer-Assisted Intervention

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 12-20 | Cite as

Unsupervised Myocardial Segmentation for Cardiac MRI

  • Anirban Mukhopadhyay
  • Ilkay Oksuz
  • Marco Bevilacqua
  • Rohan Dharmakumar
  • Sotirios A. Tsaftaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

Though unsupervised segmentation was a de-facto standard for cardiac MRI segmentation early on, recently cardiac MRI segmentation literature has favored fully supervised techniques such as Dictionary Learning and Atlas-based techniques. But, the benefits of unsupervised techniques e.g., no need for large amount of training data and better potential of handling variability in anatomy and image contrast, is more evident with emerging cardiac MR modalities. For example, CP-BOLD is a new MRI technique that has been shown to detect ischemia without any contrast at stress but also at rest conditions. Although CP-BOLD looks similar to standard CINE, changes in myocardial intensity patterns and shape across cardiac phases, due to the heart’s motion, BOLD effect and artifacts affect the underlying mechanisms of fully supervised segmentation techniques resulting in a significant drop in segmentation accuracy. In this paper, we present a fully unsupervised technique for segmenting myocardium from the background in both standard CINE MR and CP-BOLD MR. We combine appearance with motion information (obtained via Optical Flow) in a dictionary learning framework to sparsely represent important features in a low dimensional space and separate myocardium from background accordingly. Our fully automated method learns background-only models and one class classifier provides myocardial segmentation. The advantages of the proposed technique are demonstrated on a dataset containing CP-BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across 10 canine subjects, where our method outperforms state-of-the-art supervised segmentation techniques in CP-BOLD MR and performs at-par for standard CINE MR.

Keywords

Unsupervised Segmentation Dictionary Learning BOLD CINE MRI 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Anirban Mukhopadhyay
    • 1
  • Ilkay Oksuz
    • 1
  • Marco Bevilacqua
    • 1
  • Rohan Dharmakumar
    • 2
  • Sotirios A. Tsaftaris
    • 1
    • 3
  1. 1.IMT Institute for Advanced Studies LuccaLuccaItaly
  2. 2.Biomedical Imaging Research InstituteCedars-Sinai MedicalLos AngelesUSA
  3. 3.Department of Electrical Engineering and Computer ScienceNorthwestern UniversityEvanstonUSA

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