title: Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging.
abstract: BACKGROUND
  Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion.
  
  
  METHODS
  Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract.
  
  
  RESULTS
  The mean differences between automatic segmentation and manual delineation were EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g in the test set.
  
  
  CONCLUSIONS
  The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.
authors.affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden ; Department of Numerical Analysis, Centre for Mathematical Sciences, Faculty of Engineering, Lund University, 221 00 Lund, Sweden.
authors.forename: Jane
authors.initials: J
authors.lastname: Tufvesson
authors.affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden ; Department of Diagnostic Radiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden.
authors.forename: Erik
authors.initials: E
authors.lastname: Hedström
authors.affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden.
authors.forename: Katarina
authors.initials: K
authors.lastname: Steding-Ehrenborg
authors.affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden.
authors.forename: Marcus
authors.initials: M
authors.lastname: Carlsson
authors.affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden.
authors.forename: Håkan
authors.initials: H
authors.lastname: Arheden
authors.affiliation: Department of Clinical Physiology, Lund University Hospital, Lund University, 221 85 Lund, Sweden ; Department of Numerical Analysis, Centre for Mathematical Sciences, Faculty of Engineering, Lund University, 221 00 Lund, Sweden ; Department of Biomedical Engineering, Faculty of Engineering, Lund University, 221 00 Lund, Sweden.
authors.forename: Einar
authors.initials: E
authors.lastname: Heiberg
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011859:Radiography