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Segmentation of Cardiac Structures

  • Claudio Fabbri
  • Maddalena Valinoti
  • Cristiana CorsiEmail author
  • Martino Alessandrini
Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

Medical image-based 3D cardiac models have grown rapidly in the last fifteen years due to the advance and consolidation of imaging systems such as magnetic resonance, computed tomography and real-time 3D echocardiography. One of the most challenging task in the development of a 3D cardiac model from in vivo imaging is the segmentation of cardiac structures. This task is the first step towards the analysis of heart anatomy and function. Whole heart segmentation focuses on the localization and detection of the following regions: the left ventricle (LV), which starts at the mitral valve and is composed of the main LV chamber up to the apex and stops at the aortic valve; the right ventricle (RV), which begins at the tricuspid valve, comprises the RV chamber and ends at the pulmonary valves; the left atrium (LA), which start at the pulmonary veins and ends at the mitral valve; the right atrium (RA), which start at the superior and inferior vena cava and ends at the tricuspid valve. In recent years, a variety of methods have been developed for cardiac structure segmentation paving the way for “personalized” medicine in the clinical setting. In this chapter we will briefly review the most recent and advanced approaches for cardiac image segmentation. The chapter is organized in three main sections dealing with the imaging modalities used in clinical practice for cardiac structure and function assessment. A fourth section is focused on the open access validation tools for cardiac structure segmentation nowadays available. Each section is divided in paragraphs in which we will describe the different techniques developed for the specific cardiac structures.

Notes

Acknowledgements

Martino Alessandrini received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 659082.

References

  1. 1.
    Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1(4):321–331Google Scholar
  2. 2.
    Terzopoulos D, Fleischer K (1988) Deformable models. Vis Comput 4:306–331Google Scholar
  3. 3.
    Terzopoulos D, Witkin A, Kass M (1988) Constraints on deformable models: recovering 3D shape and non-rigid motion. Artif Intell 36(1):91–123zbMATHGoogle Scholar
  4. 4.
    Paragios N (2002) A variational approach for the segmentation of the left ventricle in cardiac image analysis. Int J Comput Vis 50(3):345–364MathSciNetzbMATHGoogle Scholar
  5. 5.
    Corsi C, Lamberti C, Catalano O et al (2005) Improved quantification of left ventricular volumes and mass based on endocardial and epicardial surface detection from cardiac MR images using level set models. J Cardiovasc Magn Reson 7(3):595–602Google Scholar
  6. 6.
    Lynch M, Ghita O, Whelan PF (2008) Segmentation of the left ventricle of the heart in 3-D + T MRI data using an optimized nonrigid temporal model. IEEE Trans Med Imaging 27(2):195–205Google Scholar
  7. 7.
    Schaerer J, Casta C, Pousin J, Clarysse P (2010) A dynamic elastic model for segmentation and tracking of the heart in MR image sequences. Med Image Anal 14(6):738–774Google Scholar
  8. 8.
    Constantinides C, Chenoune Y, Kachenoura N et al (2009) Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models. MIDAS J-Cardiac MR Left Ventricle Segm ChallGoogle Scholar
  9. 9.
    Ringenberg J, Deo M, Devabhaktuni V et al (2012) Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology from in vivo MRI. Meas Sci Technol 23(12):125407Google Scholar
  10. 10.
    Wu Y, Wang Y, Jia Y (2013) Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model. Comput Vis Image Underst 117(9):990–100Google Scholar
  11. 11.
    Cordero-Grande L, Vegas-Sánchez-Ferrero G, Casaseca-dela-Higuera P et al (2011) Unsupervised 4D myocardium segmentation with a Markov random field based deformable model. Med Image Anal 15(3):283–301Google Scholar
  12. 12.
    Khalifa F, Beache GM, Farb GG et al (2012) Accurate automatic analysis of cardiac cine images. IEEE Trans Biomed Eng 59(2):445–457Google Scholar
  13. 13.
    Queirós S, Barbosa D, Heyde B, Morais P, Vilaça JL, Friboulet D, D’hooge J (2014) Fast automatic myocardial segmentation in 4D cine {CMR} datasets. Med Image Anal 18(7):1115–1131Google Scholar
  14. 14.
    Queirós S, Barbosa D, Engvall J, Ebbers T, Nagel E, Sarvari, SI, D’hooge J (2015) Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets. Eur Heart J Cardiovasc ImagingGoogle Scholar
  15. 15.
    Mitchell SC, Bosch JG, Lelieveldt BP et al (2002) 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Trans Med Imaging 21(9):1167–117Google Scholar
  16. 16.
    Van Assen HC, Danilouchkine MG, Frangi A et al (2006) SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10(2):286–305Google Scholar
  17. 17.
    Lekadir K, Merrifield R, Yang GZ (2007) Outlier detection and handling for robust 3-D active shape models search. IEEE Trans Med Imaging 26(2):212–224Google Scholar
  18. 18.
    Nambakhsh CM, Yuan J, Punithakumar K et al (2013) Left ventricle segmentation in MRI via convex relaxed distribution matching. Med Image Anal 17(8):1010–1024Google Scholar
  19. 19.
    Alba X, Ventura F, Rosa M et al (2014) Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints. Magn Reson Med 72(6):1775–1784Google Scholar
  20. 20.
    Caiani EG, Colombo A, Pepi M et al (2014) Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes. Europace 16(Suppl 4):iv96–iv101Google Scholar
  21. 21.
    Qin X, Tian Y, Yan P (2015) Feature competition and partial sparse shape modeling for cardiac image sequences segmentation. Neurocomputing 149:904–915Google Scholar
  22. 22.
    Maier OM, Jiménez D, Santos A, Ledesma-Carbayo MJ (2012) Segmentation of RV in 4D cardiac MR volumes using region merging graph cuts. In: Computing in cardiology. IEEE, pp 697–702Google Scholar
  23. 23.
    Mahapatra D (2013) Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors. J Digit Imaging 26(4):721–732Google Scholar
  24. 24.
    Wang L, Lekadir K, Lee SR et al (2013) A general framework for context-specific image segmentation using reinforcement learning. IEEE Trans Med Imaging 32(5):943–958Google Scholar
  25. 25.
    Catalano O, Corsi C, Antonaci S et al (2007) Improved reproducibility of right ventricular volumes and function estimation from cardiac magnetic resonance images using level set models. Magn Reson Med 57(3):600–605Google Scholar
  26. 26.
    Liu Y, Captur G, Moon JC et al (2016) Distance regularized two level sets for segmentation of left and right ventricles from cine-MRI. Magn Reson Imaging 34(5):699–706Google Scholar
  27. 27.
    Grosgeorge D, Petitjean C, Dacher S et al (2013) Graph cut segmentation with a statistical shape model in cardiac MRI. Comput Vis Image Underst 117(9):1027–1037Google Scholar
  28. 28.
    Ou Y, Doshi J, Erus G et al (2012) Multi-atlas segmentation of the right ventricle in cardiac MRI. In: Proceedings of MICCAI RV segmentation challengeGoogle Scholar
  29. 29.
    Oghli MG, Dehlaghi V, Zadeh AM et al (2014) Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape based deformable model. J Med Signals Sens 4(3):211Google Scholar
  30. 30.
    Petitjean C, Zuluaga MA, Bai W, Dacher JN, Grosgeorge D, Caudron J, Yuan J (2015) Right ventricle segmentation from cardiac MRI: a collation study. Med Image Anal 19(1):187–202Google Scholar
  31. 31.
    Karim R, Housden RJ, Balasubramaniam M et al (2013) Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge. J Cardiovasc Magn Reson 20(15):105Google Scholar
  32. 32.
    Tobon-Gomez C, Geers A, Peters J et al (2015) Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Trans Med Imaging.  https://doi.org/10.1109/TMI.2015.2398818Google Scholar
  33. 33.
    Tao Q, Ipek EG, Shahzad R et al (2016) Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: towards objective atrial scar assessment. J Magn Reson Imaging 44(2):346–354Google Scholar
  34. 34.
    Wachinger C, Fritscher K, Sharp G et al (2015) Contour-driven atlas-based segmentation. IEEE Trans Med Imaging 34(12):2492–2505Google Scholar
  35. 35.
    Kutra D, Saalbach A, Lehmann et al (2012) Automatic multi-model-based segmentation of the left atrium in cardiac MRI scans. Med Image Comput Comput Assist Interv 15(Pt 2):1–8Google Scholar
  36. 36.
    Veni G, Fu Z, Awate SP et al (2013) Bayesian segmentation of atrium wall using globally-optimal graph cuts on 3D meshes. Inf Process Med Imaging 23:656–667Google Scholar
  37. 37.
    Zhu L, Gao Y, Yezzi A et al (2013) Automatic segmentation of the left atrium from MR images via variational region growing with a moments-based shape prior. IEEE Trans Image Process 22(12):5111–5122MathSciNetzbMATHGoogle Scholar
  38. 38.
    Valinoti M, Fabbri C, Turco D et al (2016) Development of 3D patient-specific models for left atrium geometric characterization to support ablation in atrial fibrillation patients, vol 43. IEEE Press, pp 77–80Google Scholar
  39. 39.
    Dewi EO, Abduljabbar HN, Supriyanto E (2014) Review on advanced techniques in 2-D fetal echocardiography: an image processing perspective. In: Advances in medical diagnostic technology. Springer Singapore, Singapore, pp 53–74Google Scholar
  40. 40.
    Leung KYE, Bosch JG (2010) Automated border detection in three-dimensional echocardiography: principles and promises. Eur Heart J Cardiovasc Imaging 11(2):97–108Google Scholar
  41. 41.
    Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987–1010Google Scholar
  42. 42.
    Pedrosa J, Barbosa D, Almeida N et al (2016) Cardiac chamber volumetric assessment using 3D ultrasound—a review. Curr Pharm Design 22:105–121Google Scholar
  43. 43.
    Corsi C, Saracino G, Sarti A et al (2002) Left ventricular volume estimation for real-time three-dimensional echocardiography. IEEE Trans Med Imaging 21(9):1202–1208Google Scholar
  44. 44.
    Angelini ED, Homma S, Pearson G et al (2005) Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: a clinical study on right and left ventricles. Ultrasound Med Biol 31(9):1143–1158Google Scholar
  45. 45.
    Barbosa D, Dietenbeck T, Schaerer J et al (2012) B-spline explicit active surfaces: an efficient framework for real-time 3-D region-based segmentation. IEEE Trans Image Process 21(1):241–251MathSciNetzbMATHGoogle Scholar
  46. 46.
    Orderud F, Rabben SI (2008) Real-time 3D segmentation of the left ventricle using deformable subdivision surfaces. In: IEEE conference on computer vision pattern recognition, pp 1–8Google Scholar
  47. 47.
    Hansegard J, Orderud F, Rabben SI (2007) Real time active shape models for segmentation of 3D cardiac ultrasound. In: Proceedings of the 12th international conference on computer analysis of images and patterns, pp 157–164Google Scholar
  48. 48.
    Cootes TF, Taylor CJ, Cooper DH (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59Google Scholar
  49. 49.
    Yang L, Georgescu B, Zheng Y, Meer P, Comaniciu D (2008) 3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers. In: 2008 IEEE conference on computer vision and pattern recognition, Anchorage, AK, pp 1–8Google Scholar
  50. 50.
    Milletari F, Yigitsoy M, Navab N (2014) Left ventricle segmentation in cardiac ultrasound using hough-forests with implicit shape and appearance priors. Midas J 49–56. http://hdl.handle.net/10380/3485
  51. 51.
    Oktay O, Gomez A, Keraudren K et al (2015) Probabilistic edge map (PEM) for 3D ultrasound image registration and multi-atlas left ventricle segmentation. In: van Assen H, Bovendeerd P, Delhaas T (eds) Functional imaging and modeling of the heart. Proceedings of the 8th international conference, FIMH 2015, Maastricht, The Netherlands, 25–27 June 2015, pp 223–230Google Scholar
  52. 52.
    Lempitsky V et al (2009) Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. Lecture notes in computer science (including subseries Lecture notes artificial intelligence, Lecture notes bioinformatics), vol 5528, pp 447–456Google Scholar
  53. 53.
    Bernard O, Bosch JG, Heyde B et al (2016) Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography. IEEE Trans Med Imaging 35(4):967–977Google Scholar
  54. 54.
    Engås A (2008) Segmentation of right ventricle in 3D ultrasound recordings. PhD thesis, NTNU, Trondheim, NorwayGoogle Scholar
  55. 55.
    Almeida N, Friboulet D, Sarvari SI et al (2016) Left-atrial segmentation from 3-D ultrasound using B-spline explicit active surfaces with scale uncoupling. IEEE Trans Ultrason Ferroelectr Freq Control 63(2):212–221Google Scholar
  56. 56.
    Almeida N, Papachristidis A, Pearson P et al (2016) Left atrial volumetric assessment using a novel automated framework for 3D echocardiography: a multi-centre analysis. Eur Heart J Cardiovasc Imaging [Epub ahead of print]Google Scholar
  57. 57.
    Voigt I, Mansi T, Mihalef V et al (2011) Patient-specific model of left heart anatomy, dynamics and hemodynamics from 4D TEE: a first validation study. Lecture notes in computer science (including subseries Lecture notes artificial intelligence, Lecture notes bioinformatics), vol 6666, pp 341–349Google Scholar
  58. 58.
    Tsang W, Salgo IS, Zarochev L et al (2013) Fully automated quantification of left ventricular and left atrial volumes from transthoracic 3D echocardiography: a validation study. J Am Coll Cardiol 61(10):E904Google Scholar
  59. 59.
    Smith-Bindman R, Lipson J, Marcus R et al (2009) Radiation dose associated with common computed tomography exams and the associated lifetime attributed risk of cancer. Arch Intern Med 169(22):2078–2086Google Scholar
  60. 60.
    Greenland P, Bonow RO, Brundage BH et al (2007) Coronary artery calcium scoring: ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain. J Am Coll Cardiol 49:378–402Google Scholar
  61. 61.
    Markham R, Murdoch D, Walters D et al (2016) Coronary computed tomography angiography and its increasing application in day to day cardiology practice. Intern Med J 46(1):29–34Google Scholar
  62. 62.
    Ecabert O, Peters J, Weese J, et al (2006) Automatic heart segmentation in CT: current and future applications. MedicaMundi. 50:12–13Google Scholar
  63. 63.
    Schoenhagen P, Halliburton SS, Stillman AE et al (2005) CT of the heart: Principles, advances, clinical uses. Clevel Clin J Med 72(2):127–138Google Scholar
  64. 64.
    Zhuang X (2013) Challenges and methodologies of fully automatic whole heart segmentation: a review. J Health Eng 4(3):371–408Google Scholar
  65. 65.
    Zheng Y, Barbu A, Georgescu B et al (2008) Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features. IEEE Trans Med Imaging 27(11):1668–1681Google Scholar
  66. 66.
    Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22(8):986–1004Google Scholar
  67. 67.
    Suri JS (2000) Computer vision, pattern recognition and image processing in left ventricle segmentation: the last 50 years. Pattern Anal Appl 3(3):209–242zbMATHGoogle Scholar
  68. 68.
    Zhu L, Gao Y, Appia V et al (2013) Automatic delineation of the myocardial wall from ct images via shape segmentation and variational region growing. IEEE Trans Biomed Eng 60(10):2887–2895Google Scholar
  69. 69.
    Saruhassini K, Vanithamani R (2015) An efficient system for automatic heart wall segmentation from cardiac CT images. Int J Adv Res Comput Sci Manag Stud 3(4):316–326Google Scholar
  70. 70.
    European Carotid Surgery Trialists Collaborative Group (1999) Randomised trial of endarterectomy for recently symptomatic carotid stenosis: final results of the MRC european carotid surgery (ECST). Lancet 351:1379–1387Google Scholar
  71. 71.
    Dehkordi MT, Sadri S, Doosthoseini A (2011) A review of coronary vessel segmentation algorithms. J Med Signals Sens 1(1):49–54Google Scholar
  72. 72.
    Tian Y, Pan Y, Duan F, et al (2016) Automated segmentation of coronary arteries based on statistical region growing and heuristic decision method. BioMed Res Int. Article ID 3530251Google Scholar
  73. 73.
    Zhou C, Chan HP, Chughtai A et al (2012) Automated coronary artery tree extraction in coronary CT angiography using a multiscale enhancement and dynamic balloon tracking (MSCAR-DBT) method. Comput Med Imaging Graph Off J Comput Med Imaging Soc 36(1):1–10Google Scholar
  74. 74.
    Peng P, Lekadir K, Gooya A et al (2016) A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magma 29:155–195Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Claudio Fabbri
    • 1
  • Maddalena Valinoti
    • 1
  • Cristiana Corsi
    • 1
    Email author
  • Martino Alessandrini
    • 1
  1. 1.BIOMedical Imaging Group, Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione (DEI)Università di BolognaBolognaItaly

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