title: Automated interventricular septum segmentation for black-blood myocardial T2* measurement in thalassemia.
abstract: OBJECTIVE
  To develop and validate an automated segmentation method that extracts the interventricular septum (IS) from myocardial black-blood images for the T2* measurement in thalassemia patients.
  
  
  METHODS
  A total of 144 thalassemia major patients (age range, 11-51 years; 73 males) were scanned with a black-blood multi-echo gradient-echo sequence using a 1.5 Tesla Siemens Sonata system (flip angle 20°, sampling bandwidth 810 Hz/pixel, voxel size 1.56 × 1.56 × 10 mm(3) and variable fields of view (20-30) × 40 cm(2) depending on patient size). The improved Chan-Vese model with an automated initialization by the circular Hough transformation was implemented to segment the endocardial and epicardial margins of the left ventricle (LV). Consequently, the IS was extracted by analyzing the anatomical relation between the LV and the blood pool of the right ventricle, identified by intensity thresholding. The proposed automated IS segmentation (AISS) method was compared with the conventional manual method by using the Bland-Altman analysis and the coefficient of variation (CoV).
  
  
  RESULTS
  The T2* measurements using the AISS method were in good agreement with those manually measured by experienced observers with a mean difference of 1.71% and a CoV of 4.15% (P < 0.001).
  
  
  CONCLUSIONS
  Black-blood myocardial T2* measurement can be fully automated with the proposed AISS method.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Zhengzhou University of Light Industry, Zhengzhou, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Zheng
authors.forename: Yanqiu
authors.initials: Y
authors.lastname: Feng
authors.forename: Xiaping
authors.initials: X
authors.lastname: Wei
authors.forename: Meiyan
authors.initials: M
authors.lastname: Feng
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.forename: Zhentai
authors.initials: Z
authors.lastname: Lu
authors.forename: Yikai
authors.initials: Y
authors.lastname: Xu
authors.forename: Hongwen
authors.initials: H
authors.lastname: Chen
authors.forename: Taigang
authors.initials: T
authors.lastname: He
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D005260:Female; D006346:Heart Septum / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D013789:Thalassemia / Q000473:pathology*; D054088:Ventricular Septum / Q000502:physiology*; D055815:Young Adult
keywords: automated septum segmentation; black-blood imaging; iron overload; magnetic resonance (MR); myocardial T2*