• DocumentCode
    561839
  • Title

    Semi-automated border detection for right ventricular volume estimation from MR images

  • Author

    Carminati, Maria C. ; Gripari, Paola ; Maffessanti, Francesco ; Corsi, Cristiana ; Pontone, Gianluca ; Andreini, Daniele ; Pepi, Mauro ; Caiani, Enrico G.

  • Author_Institution
    Biomed. Eng. Dept., Politec. di Milano, Milan, Italy
  • fYear
    2011
  • fDate
    18-21 Sept. 2011
  • Firstpage
    381
  • Lastpage
    384
  • Abstract
    Two different methods for semi-automated right ventricular (RV) endocardial border detection from MR images, based on different implementation of level set technique, have been developed and validated. Dynamic, ECG-gated, steady-state free precession short axis images were obtained in 26 consecutive patients. An expert cardiologist provided the “gold standard” for RV dimensions, by manually tracing the endocardial contours. Semi-automated detection was applied to obtain RV end-diastolic and end-systolic volumes, as well as stroke volume and ejection fraction. Comparison with “gold standard” was performed by linear regression and Bland-Altman analyses. Results showed high correlations and small biases and narrow limits of agreement with the “gold standard” values. Both methods provided reliable measurements of RV dimensions; however, better accuracy is related to higher manual interaction.
  • Keywords
    biomedical MRI; diseases; electrocardiography; medical image processing; Bland-Altman analysIs; ECG-gated steady-state free precession short axis images; MR image; RV dimensions; RV end-diastolic volume; RV end-systolic volume; ejection fraction; endocardial contour tracing; gold standard value; level set technique; linear regression; right ventricular volume estimation; semiautomated border detection; stroke volume; Cavity resonators; Correlation; Gold; Image edge detection; Level set; Muscles; Volume measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing in Cardiology, 2011
  • Conference_Location
    Hangzhou
  • ISSN
    0276-6547
  • Print_ISBN
    978-1-4577-0612-7
  • Type

    conf

  • Filename
    6164582