title: Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming.
abstract: OBJECTIVE
  Segmentation of the left ventricle (LV) is very important in the assessment of cardiac functional parameters. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic LV segmentation on short-axis cardiac magnetic resonance images (MRI).
  
  
  METHODS
  The database used in this study consists of 45 cases obtained from the Sunnybrook Health Sciences Centre. The 45 cases contain 12 ischemic heart failures, 12 non-ischemic heart failures, 12 LV hypertrophies, and 9 normal cases. Three key techniques are developed in this segmentation algorithm: 1) topological stable-state thresholding method is proposed to refine the endocardial contour, 2) an edge map with non-maxima gradient suppression approach, and 3) a region-restricted technique that is proposed to improve the dynamic programming to derive the epicardial boundary.
  
  
  RESULTS
  The validation experiments were performed on a pool of data sets of 45 cases. For both endo- and epicardial contours of our results, percentage of good contours is about 91%, the average perpendicular distance is about 2 mm, and the overlapping dice metric is about 0.91. The regression and determination coefficient for the experts and our proposed method on the ejection fraction is 1.05 and 0.9048, respectively; they are 0.98 and 0.8221 for LV mass.
  
  
  CONCLUSIONS
  An automatic method using topological stable-state thresholding and region restricted dynamic programming has been proposed to segment left ventricle in short-axis cardiac MRI. Evaluation results indicate that the proposed segmentation method can improve the accuracy and robust of left ventricle segmentation. The proposed segmentation approach shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.
authors.affiliation: Center for Biomedical Imaging and Bioinformatics, Key Laboratory of Education Ministry for Image Processing and Intelligence Control, School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luo Yu Road, Wuhan, Hubei, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
authors.forename: Huaifei
authors.initials: H
authors.lastname: Hu
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Xu
authors.forename: Enmin
authors.initials: E
authors.lastname: Song
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011382:Programming, Linear; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018487:Ventricular Dysfunction, Left / Q000473:pathology*