title: A 3-D Active Contour Method for Automated Segmentation of the Left Ventricle From Magnetic Resonance Images.
abstract: This study's objective is to develop and validate a fast automated 3-D segmentation method for cardiac magnetic resonance imaging (MRI). The segmentation algorithm automatically reconstructs cardiac MRI DICOM data into a 3-D model (i.e., direct volumetric segmentation), without relying on prior statistical knowledge.
  
  
  
  A novel 3-D active contour method was employed to detect the left ventricular cavity in 33 subjects with heterogeneous heart diseases from the York University database. Papillary muscles were identified and added to the chamber using a convex hull of the left ventricle and interpolation. The myocardium was then segmented using a similar 3-D segmentation method according to anatomic information. A multistage approach was taken to determine the method's efficacy.
  
  
  
  Our method demonstrated a significant improvement in segmentation performance when compared to manual segmentation and other automated methods.
  
  
  
  A true 3-D reconstruction technique without the need for training datasets or any user-driven segmentation has been developed. In this method, a novel combination of internal and external energy terms for active contour was utilized that exploits histogram matching for improving the segmentation performance. This method takes advantage of full volumetric imaging, does not rely on prior statistical knowledge, and employs a convex-hull interpolation to include the papillary muscles.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Hajiaghayi
authors.forename: Elliott M
authors.initials: EM
authors.lastname: Groves
authors.forename: Hamid
authors.initials: H
authors.lastname: Jafarkhani
authors.forename: Arash
authors.initials: A
authors.lastname: Kheradvar
mesh_terms: D000465:Algorithms; D006352:Heart Ventricles / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity