European Radiology

, Volume 26, Issue 5, pp 1503–1511 | Cite as

Clinical feasibility of a myocardial signal intensity threshold-based semi-automated cardiac magnetic resonance segmentation method

  • Akos Varga-Szemes
  • Giuseppe Muscogiuri
  • U. Joseph Schoepf
  • Julian L. Wichmann
  • Pal Suranyi
  • Carlo N. De Cecco
  • Paola M. Cannaò
  • Matthias Renker
  • Stefanie Mangold
  • Mary A. Fox
  • Balazs Ruzsics



To assess the accuracy and efficiency of a threshold-based, semi-automated cardiac MRI segmentation algorithm in comparison with conventional contour-based segmentation and aortic flow measurements.


Short-axis cine images of 148 patients (55 ± 18 years, 81 men) were used to evaluate left ventricular (LV) volumes and mass (LVM) using conventional and threshold-based segmentations. Phase-contrast images were used to independently measure stroke volume (SV). LV parameters were evaluated by two independent readers.


Evaluation times using the conventional and threshold-based methods were 8.4 ± 1.9 and 4.2 ± 1.3 min, respectively (P < 0.0001). LV parameters measured by the conventional and threshold-based methods, respectively, were end-diastolic volume (EDV) 146 ± 59 and 134 ± 53 ml; end-systolic volume (ESV) 64 ± 47 and 59 ± 46 ml; SV 82 ± 29 and 74 ± 28 ml (flow-based 74 ± 30 ml); ejection fraction (EF) 59 ± 16 and 58 ± 17 %; and LVM 141 ± 55 and 159 ± 58 g. Significant differences between the conventional and threshold-based methods were observed in EDV, ESV, and LVM mesurements; SV from threshold-based and flow-based measurements were in agreement (P > 0.05) but were significantly different from conventional analysis (P < 0.05). Excellent inter-observer agreement was observed.


Threshold-based LV segmentation provides improved accuracy and faster assessment compared to conventional contour-based methods.

Key Points

Threshold-based left ventricular segmentation provides time-efficient assessment of left ventricular parameters

The threshold-based method can discriminate between blood and papillary muscles

This method provides improved accuracy compared to aortic flow measurements as a reference


Left ventricular function Left ventricular mass Aortic flow Cine magnetic resonance imaging Semi-automated segmentation 



The scientific guarantor of this publication is UJS. The authors of this manuscript declare relationships with the following companies: UJS is a consultant for and/or receives research support from Bayer, Bracco, GE Healthcare, Medrad, and Siemens Healthcare. The other authors declare that they have no competing interests. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional review board approval was obtained. Written informed consent was waived by the institutional review board. None of the study subjects or cohorts have been previously reported. Methodology: retrospective, diagnostic/experimental study, performed at one institution.

Conflict of interest

U.J.S. is a consultant for and/or receives research support from Bayer (Wayne/NJ, USA), Bracco (Princeton/NJ, USA), GE Healthcare (Little Chalfont, UK), Medrad (Warrendale/PA, USA), and Siemens Healthcare (Malvern/PA, USA).


  1. 1.
    Grothues F, Smith GC, Moon JC et al (2002) Comparison of interstudy reproducibility of cardiovascular magnetic resonance with two-dimensional echocardiography in normal subjects and in patients with heart failure or left ventricular hypertrophy. Am J Cardiol 90:29–34CrossRefPubMedGoogle Scholar
  2. 2.
    Alfakih K, Plein S, Thiele H, Jones T, Ridgway JP, Sivananthan MU (2003) Normal human left and right ventricular dimensions for MRI as assessed by turbo gradient echo and steady-state free precession imaging sequences. J Magn Reson Imaging 17:323–329CrossRefPubMedGoogle Scholar
  3. 3.
    Papavassiliu T, Kuhl HP, Schroder M et al (2005) Effect of endocardial trabeculae on left ventricular measurements and measurement reproducibility at cardiovascular MR imaging. Radiology 236:57–64CrossRefPubMedGoogle Scholar
  4. 4.
    Chuang ML, Gona P, Hautvast GL et al (2012) Correlation of trabeculae and papillary muscles with clinical and cardiac characteristics and impact on CMR measures of LV anatomy and function. JACC Cardiovasc Imaging 5:1115–1123CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Weinsaft JW, Cham MD, Janik M et al (2008) Left ventricular papillary muscles and trabeculae are significant determinants of cardiac MRI volumetric measurements: effects on clinical standards in patients with advanced systolic dysfunction. Int J Cardiol 126:359–365CrossRefPubMedGoogle Scholar
  6. 6.
    Petitjean C, Dacher JN (2011) A review of segmentation methods in short axis cardiac MR images. Med Image Anal 15:169–184CrossRefPubMedGoogle Scholar
  7. 7.
    Jaspers K, Freling HG, van Wijk K, Romijn EI, Greuter MJ, Willems TP (2013) Improving the reproducibility of MR-derived left ventricular volume and function measurements with a semi-automatic threshold-based segmentation algorithm. Int J Cardiovasc Imaging 29:617–623CrossRefPubMedGoogle Scholar
  8. 8.
    Mahnken AH, Muhlenbruch G, Koos R et al (2006) Automated vs. manual assessment of left ventricular function in cardiac multidetector row computed tomography: comparison with magnetic resonance imaging. Eur Radiol 16:1416–1423CrossRefPubMedGoogle Scholar
  9. 9.
    Jeltsch M, Ranft S, Klass O, Aschoff AJ, Hoffmann MH (2008) Evaluation of accordance of magnetic resonance volumetric and flow measurements in determining ventricular stroke volume in cardiac patients. Acta Radiol 49:530–539CrossRefPubMedGoogle Scholar
  10. 10.
    Gatehouse PD, Rolf MP, Graves MJ et al (2010) Flow measurement by cardiovascular magnetic resonance: a multi-center multi-vendor study of background phase offset errors that can compromise the accuracy of derived regurgitant or shunt flow measurements. J Cardiovasc Magn Reson 12:5CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Misra N, Shah AM, Lai WW (2011) Correction of phase offset errors in cardiovascular magnetic resonance using background subtraction from stationary tissue. J Cardiovasc Magn Reson 13(Suppl 1):P213CrossRefPubMedCentralGoogle Scholar
  12. 12.
    Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310CrossRefPubMedGoogle Scholar
  13. 13.
    Codella NC, Weinsaft JW, Cham MD, Janik M, Prince MR, Wang Y (2008) Left ventricle: automated segmentation by using myocardial effusion threshold reduction and intravoxel computation at MR imaging. Radiology 248:1004–1012CrossRefPubMedGoogle Scholar
  14. 14.
    Codella NC, Cham MD, Wong R et al (2010) Rapid and accurate left ventricular chamber quantification using a novel CMR segmentation algorithm: a clinical validation study. J Magn Reson Imaging 31:845–853CrossRefPubMedGoogle Scholar
  15. 15.
    Lu YL, Connelly KA, Dick AJ, Wright GA, Radau PE (2013) Automatic functional analysis of left ventricle in cardiac cine MRI. Quant Imaging Med Surg 3:200–209PubMedPubMedCentralGoogle Scholar
  16. 16.
    Kurkure U, Pednekar A, Muthupillai R, Flamm SD, Kakadiaris Ast IA (2009) Localization and segmentation of left ventricle in cardiac cine-MR images. IEEE Trans Biomed Eng 56:1360–1370CrossRefPubMedGoogle Scholar
  17. 17.
    Rezaee MR, van der Zwet PJ, Lelieveldt BP, van der Geest RJ, Reiber JH (2000) A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE Trans Image Process 9:1238–1248CrossRefPubMedGoogle Scholar
  18. 18.
    Kaus MR, von Berg J, Weese J, Niessen W, Pekar V (2004) Automated segmentation of the left ventricle in cardiac MRI. Med Image Anal 8:245–254CrossRefPubMedGoogle Scholar
  19. 19.
    Cordero-Grande L, Vegas-Sanchez-Ferrero G, Casaseca-de-la-Higuera P et al (2011) Unsupervised 4D myocardium segmentation with a Markov random field based deformable model. Med Image Anal 15:283–301CrossRefPubMedGoogle Scholar
  20. 20.
    Mitchell SC, Lelieveldt BP, van der Geest RJ, Bosch HG, Reiber JH, Sonka M (2011) Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 20:415–423CrossRefGoogle Scholar
  21. 21.
    O'Brien SP, Ghita O, Whelan PF (2011) A novel model-based 3D + time left ventricular segmentation technique. IEEE Trans Med Imaging 30:461–474CrossRefPubMedGoogle Scholar
  22. 22.
    Paragios N (2003) A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans Med Imaging 22:773–776CrossRefPubMedGoogle Scholar
  23. 23.
    Ben Ayed I, Lu Y, Li S, Ross I (2008) Left ventricle tracking using overlap priors. Med Image Comput Comput Assist Interv 11(Pt 1):1025–1033PubMedGoogle Scholar
  24. 24.
    Lin X, Cowan B, Young A (2005) Model-based graph cut method for segmentation of the left ventricle. Conf Proc IEEE Eng Med Biol Soc 3:3059–3062PubMedGoogle Scholar
  25. 25.
    Cocosco CA, Niessen WJ, Netsch T et al (2008) Automatic image-driven segmentation of the ventricles in cardiac cine MRI. J Magn Reson Imaging 28:366–374CrossRefPubMedGoogle Scholar
  26. 26.
    Codella NC, Lee HY, Fieno DS et al (2012) Improved left ventricular mass quantification with partial voxel interpolation: in vivo and necropsy validation of a novel cardiac MRI segmentation algorithm. Circ Cardiovasc Imaging 5:137–146CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Cheng JY (2011) Image based background magnetic field correction for aortic and pulmonary artery flow measurement using phase contrast. J Cardiovasc Magn Reson 13(Suppl 1):P355CrossRefPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Akos Varga-Szemes
    • 1
  • Giuseppe Muscogiuri
    • 1
    • 2
  • U. Joseph Schoepf
    • 1
  • Julian L. Wichmann
    • 1
    • 3
  • Pal Suranyi
    • 1
  • Carlo N. De Cecco
    • 1
  • Paola M. Cannaò
    • 1
    • 4
  • Matthias Renker
    • 1
    • 5
  • Stefanie Mangold
    • 1
    • 6
  • Mary A. Fox
    • 1
  • Balazs Ruzsics
    • 7
  1. 1.Division of Cardiovascular Imaging, Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  2. 2.Department of Medical-Surgical Sciences and Translational MedicineUniversity of Rome “Sapienza”RomeItaly
  3. 3.Department of Diagnostic and Interventional RadiologyUniversity Hospital FrankfurtFrankfurtGermany
  4. 4.Scuola di Specializzazione in RadiodiagnosticaUniversity of MilanMilanItaly
  5. 5.Kerckhoff Heart and Thorax CenterBad NauheimGermany
  6. 6.Department of Diagnostic and Interventional RadiologyEberhard-Karls University TuebingenTuebingenGermany
  7. 7.Department of CardiologyRoyal Liverpool and Broadgreen University HospitalsLiverpoolUK

Personalised recommendations