Integrating Atlas and Graph Cut Methods for Left Ventricle Segmentation from Cardiac Cine MRI

Conference paper

DOI: 10.1007/978-3-319-52718-5_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10124)
Cite this paper as:
Dangi S., Cahill N., Linte C.A. (2017) Integrating Atlas and Graph Cut Methods for Left Ventricle Segmentation from Cardiac Cine MRI. In: Mansi T., McLeod K., Pop M., Rhode K., Sermesant M., Young A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2016. Lecture Notes in Computer Science, vol 10124. Springer, Cham


Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care imaging modality for cardiac morphology, function assessment, and guidance of cardiac interventions. All these applications rely on accurate extraction of the myocardial tissue and blood pool from the imaging data. Here we propose a framework for left ventricle (LV) segmentation from cardiac cine MRI. First, we segment the LV blood pool using iterative graph cuts, and subsequently use this information to segment the myocardium. We formulate the segmentation procedure as an energy minimization problem in a graph subject to the shape prior obtained by label propagation from an average atlas using affine registration. The proposed framework has been validated on 30 patient cardiac cine MRI datasets available through the STACOM LV segmentation challenge and yielded fast, robust, and accurate segmentation results.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shusil Dangi
    • 1
  • Nathan Cahill
    • 1
    • 3
  • Cristian A. Linte
    • 1
    • 2
  1. 1.Chester F. Carlson Center for Imaging ScienceRochester Institute of TechnologyRochesterUSA
  2. 2.Biomedical EngineeringRochester Institute of TechnologyRochesterUSA
  3. 3.Center for Applied and Computational MathematicsRochester Institute of TechnologyRochesterUSA

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