title: Left ventricular volume estimation in cardiac three-dimensional ultrasound: a semiautomatic border detection approach.
abstract: OBJECTIVE
  We propose a semiautomatic endocardial border detection method for three-dimensional (3D) time series of cardiac ultrasound (US) data based on pattern matching and dynamic programming, operating on two-dimensional (2D) slices of the 3D plus time data, for the estimation of full cycle left ventricular volume, with minimal user interaction.
  
  
  METHODS
  The presented method is generally applicable to 3D US data and evaluated on data acquired with the Fast Rotating Ultrasound (FRU-) Transducer, developed by Erasmus Medical Center (Rotterdam, the Netherlands), a conventional phased-array transducer, rotating at very high speed around its image axis. The detection is based on endocardial edge pattern matching using dynamic programming, which is constrained by a 3D plus time shape model. It is applied to an automatically selected subset of 2D images of the original data set, for typically 10 equidistant rotation angles and 16 cardiac phases (160 images). Initialization requires the drawing of four contours per patient manually. We evaluated this method on 14 patients against MRI end-diastole and end-systole volumes. Initialization requires the drawing of four contours per patient manually. We evaluated this method on 14 patients against MRI end-diastolic (ED) and end-systolic (ES) volumes.
  
  
  RESULTS
  The semiautomatic border detection approach shows good correlations with MRI ED/ES volumes (r = 0.938) and low interobserver variability (y = 1.005x - 16.7, r = 0.943) over full-cycle volume estimations. It shows a high consistency in tracking the user-defined initial borders over space and time.
  
  
  CONCLUSIONS
  We show that the ease of the acquisition using the FRU-transducer and the semiautomatic endocardial border detection method together can provide a way to quickly estimate the left ventricular volume over the full cardiac cycle using little user interaction.
authors.affiliation: Division of Image Processing, Department of Radiology, C2S, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands. marijn.vanstralen@erasmusmc.nl
authors.forename: Marijn
authors.initials: M
authors.lastname: van Stralen
authors.forename: Johan G
authors.initials: JG
authors.lastname: Bosch
authors.forename: Marco M
authors.initials: MM
authors.lastname: Voormolen
authors.forename: Gerard
authors.initials: G
authors.lastname: van Burken
authors.forename: Boudewijn J
authors.initials: BJ
authors.lastname: Krenning
authors.forename: Robert-Jan M
authors.initials: RJ
authors.lastname: van Geuns
authors.forename: Charles T
authors.initials: CT
authors.lastname: Lancée
authors.forename: Nico
authors.initials: N
authors.lastname: de Jong
authors.forename: Johan H C
authors.initials: JH
authors.lastname: Reiber
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D005260:Female; D006352:Heart Ventricles / Q000000981:diagnostic imaging; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013318:Stroke Volume; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*