title: Left ventricle: automated segmentation by using myocardial effusion threshold reduction and intravoxel computation at MR imaging.
abstract: This retrospective analysis of existing patient data had institutional review board approval and was performed in compliance with HIPAA. No informed consent was required. The purpose of the study was to develop and validate an algorithm for automated segmentation of the left ventricular (LV) cavity that accounts for papillary and/or trabecular muscles and partial voxels in cine magnetic resonance (MR) images, an algorithm called LV Myocardial Effusion Threshold Reduction with Intravoxel Computation (LV-METRIC). The algorithm was validated in biologic phantoms, and its results were compared with those of manual tracing, as well as those of a commercial automated segmentation software (MASS [MR Analytical Software System]), in 38 subjects. LV-METRIC accuracy in vitro was 98.7%. Among the 38 subjects studied, LV-METRIC and MASS ejection fraction estimations were highly correlated with manual tracing (R(2) = 0.97 and R(2) = 0.95, respectively). Ventricular volume estimations were smaller with LV-METRIC and larger with MASS than those calculated by using manual tracing, though all results were well correlated (R(2) = 0.99). LV-METRIC volume measurements without partial voxel interpolation were statistically equivalent to manual tracing results (P > .05). LV-METRIC had reduced intraobserver and interobserver variability compared with other methods. MASS required additional manual intervention in 58% of cases, whereas LV-METRIC required no additional corrections. LV-METRIC reliably and reproducibly measured LV volumes.
  
  
  BACKGROUND
  http://radiology.rsnajnls.org/cgi/content/full/248/3/1004/DC1.
authors: Department of Physiology, Biophysics, and Systems Biology, Weill Medical College of Cornell University, 575 Lexington Ave, 3rd Floor, New York, NY, USA.
authors: Noel C F
authors: NC
authors: Codella
authors: Jonathan W
authors: JW
authors: Weinsaft
authors: Matthew D
authors: MD
authors: Cham
authors: Matthew
authors: M
authors: Janik
authors: Martin R
authors: MR
authors: Prince
authors: Yi
authors: Y
authors: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; 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*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis*