title: Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints.
abstract: OBJECTIVE
  Magnetic resonance imaging (MRI), specifically late-enhanced MRI, is the standard clinical imaging protocol to assess cardiac viability. Segmentation of myocardial walls is a prerequisite for this assessment. Automatic and robust multisequence segmentation is required to support processing massive quantities of data.
  
  
  METHODS
  A generic rule-based framework to automatically segment the left ventricle myocardium is presented here. We use intensity information, and include shape and interslice smoothness constraints, providing robustness to subject- and study-specific changes. Our automatic initialization considers the geometrical and appearance properties of the left ventricle, as well as interslice information. The segmentation algorithm uses a decoupled, modified graph cut approach with control points, providing a good balance between flexibility and robustness.
  
  
  RESULTS
  The method was evaluated on late-enhanced MRI images from a 20-patient in-house database, and on cine-MRI images from a 15-patient open access database, both using as reference manually delineated contours. Segmentation agreement, measured using the Dice coefficient, was 0.81±0.05 and 0.92±0.04 for late-enhanced MRI and cine-MRI, respectively. The method was also compared favorably to a three-dimensional Active Shape Model approach.
  
  
  CONCLUSIONS
  The experimental validation with two magnetic resonance sequences demonstrates increased accuracy and versatility.
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain.
authors.forename: Xènia
authors.initials: X
authors.lastname: Albà
authors.forename: Rosa M
authors.initials: RM
authors.lastname: Figueras I Ventura
authors.forename: Karim
authors.initials: K
authors.lastname: Lekadir
authors.forename: Catalina
authors.initials: C
authors.lastname: Tobon-Gomez
authors.forename: Corné
authors.initials: C
authors.lastname: Hoogendoorn
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Frangi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018487:Ventricular Dysfunction, Left / Q000473:pathology*
keywords: cardiac; graph cut; late-enhanced magnetic resonance imaging; magnetic resonance imaging; myocardial segmentation