Skip to main content

Advertisement

SpringerLink
Log in

Overview of the Whole Heart and Heart Chamber Segmentation Methods

  • Original Article
  • Published:
Cardiovascular Engineering and Technology Aims and scope Submit manuscript

Abstract

Background

Preservation and improvement of heart and vessel health is the primary motivation behind cardiovascular disease (CVD) research. Development of advanced imaging techniques can improve our understanding of disease physiology and serve as a monitor for disease progression. Various image processing approaches have been proposed to extract parameters of cardiac shape and function from different cardiac imaging modalities with an overall intention of providing full cardiac analysis. Due to differences in image modalities, the selection of an appropriate segmentation algorithm may be a challenging task.

Purpose

This paper presents a comprehensive and critical overview of research on the whole heart, bi-ventricles and left atrium segmentation methods from computed tomography (CT), magnetic resonance (MRI) and echocardiography (echo) imaging. The paper aims to: (1) summarize the considerable challenges of cardiac image segmentation, (2) provide the comparison of the segmentation methods, (3) classify significant contributions in the field and (4) critically review approaches in terms of their performance and accuracy.

Conclusion

The methods described are classified based on the used segmentation approach into (1) edge-based segmentation methods, (2) model-fitting segmentation methods and (3) machine and deep learning segmentation methods and are further split based on the targeted cardiac structure. Edge-based methods are mostly developed as semi-automatic and allow end-user interaction, which provides physicians with extra control over the final segmentation. Model-fitting methods are very robust and resistant to the high variability in image contrast and overall image quality. Nevertheless, they are often time-consuming and require appropriate models with prior knowledge. While the emerging deep learning segmentation approaches provide unprecedented performance in some specific scenarios and under the appropriate training, their performance highly depends on the data quality and the amount and the accuracy of provided annotations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  1. Al’Aref, S. J., K. Anchouche, G. Singh, P. J. Slomka, K. K. Kolli, A. Kumar, M. Pandey, G. Maliakal, A. R. van Rosendael, A. N. Beecy, D. S. Berman, J. Leipsic, K. Nieman, D. Andreini, G. Pontone, U. J. Schoepf, L. J. Shaw, H. J. Chang, J. Narula, J. J. Bax, Y. Guan, and J. K. Min. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 40(24):1975–1986, 2019. https://doi.org/10.1093/eurheartj/ehy404.

    Article  Google Scholar 

  2. Alba, X., M. Pereanez, C. Hoogendoorn, A. Swift, J. Wild, A. Frangi, and K. Lekadir. An algorithm for the segmentation of highly abnormal hearts using a generic statistical shape model. IEEE Trans. Med. Imaging 35(3):845–859, 2015.

    Google Scholar 

  3. Almeida, N., D. Friboulet, S. I. Sarvari, O. Bernard, D. Barbosa, E. Samset, and J. Dhooge. Left-atrial segmentation from 3d ultrasound using b-spline explicit active surfaces with scale uncoupling. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 63:212–221, 2016.

    Google Scholar 

  4. Almeida, N., S. Sarvari, F. Orderud, O. Gérard, J. D’hooge, and F. Samset. Automatic left-atrial segmentation from cardiac 3d ultrasound: a dual-chamber model-based approach, pp. 970–900, 2016.

  5. Arrieta, C., S. Uribe, Long C. Sing, D. Hurtado, M. Andia, P. Irarrazaval, and C. Tejos. Simultaneous left and right ventricle segmentation using topology preserving level sets. Biomed. Signal Process. Control 33:88–95, 2017.

    Google Scholar 

  6. Bai, J., P. Li, and K. Wang. Automatic whole heart segmentation based on watershed and active contour model in CT images. In 2016 5th International Conference on Computer Science and Network Technology (ICCSNT), pp. 741–744, 2016.

  7. Bai, W., W. Shi, D. P. O’Regan, T. Tong, H. Wang, S. Jamil-Copley, N. S. Peters, and D. Rueckert. A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac mr images. IEEE Trans. Med. Imaging 32:1302–1315, 2013.

    Google Scholar 

  8. Bernard, O., A. Lalande, C. Zotti, F. Cervenansky, X. Yang, P. A. Heng, I. Cetin, K. Lekadir, O. Camara, Á. Miguel, B. González, G. Sanroma, S. Napel, S. Petersen, G. Tziritas, G. Ilias, M. Khened, V. Kollerathu, G. Krishnamurthi, M. M. Rohe, and P. M. Jodoin. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37:2514–2525, 2018.

    Google Scholar 

  9. Bian, C., X. Yang, J. Ma, S. Zheng, Y. A. Liu, R. Nezafat, P. A. Heng, and Y. Zheng. Pyramid network with online hard example mining for accurate left atrium segmentation. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, edited by M. Pop, M. Sermesant, J. Zhao, S. Li, K. McLeod, A. Young, K. Rhode, and T. Mansi. Cham: Springer, 2019, pp. 237–245.

    Google Scholar 

  10. Boyle, P., S. Zahid, and N. Trayanova. Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. Europace 18:136–145, 2016.

    Google Scholar 

  11. Cecchi, F., M. H. Yacoub, and I. Olivotto. Hypertrophic cardiomyopathy in the community: why we should care. Nat. Clin. Pract. Cardiovasc. Med. 2:324–325, 2005.

    Google Scholar 

  12. Chung, M. K., M. Refaat, W. K. Shen, V. Kutyifa, Y. M. Cha, L. Di Biase, A. Baranchuk, R. Lampert, A. Natale, J. Fisher, and D. R. Lakkireddy. Atrial fibrillation. J. Am. Coll. Cardiol. 75(14):1689–1713, 2020.

    Google Scholar 

  13. Cocosco, C., W. J. Niessen, T. Netsch, E. Vonken, G. Lund, A. Stork, and M. Viergever. Automatic image-driven segmentation of the ventricles in cardiac cine MRI. JMRI 28:366–374, 2008.

    Google Scholar 

  14. Collins, M., A. Hsieh, C. J. Ohazama, et al. Assessment of regional wall motion abnormalities with real-time 3-dimensional echocardiography. J. Am. Soc. Echocardiogr. 12(1):7–14, 1999. https://doi.org/10.1016/s0894-7317(99)70167-7.

    Article  Google Scholar 

  15. Cootes, T. F., G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6):681–685, 2001.

    Google Scholar 

  16. Cootes, T. F., and C. Taylor. Statistical models of appearance for computer vision, 2004.

  17. Corrado, D., P. J. van Tintelen, W. J. McKenna, R. N. W. Hauer, A. Anastastakis, A. Asimaki, C. Basso, B. Bauce, C. Brunckhorst, C. Bucciarelli-Ducci, F. Duru, P. Elliott, R. M. Hamilton, K. H. Haugaa, C. A. James, D. Judge, M. S. Link, F. E. Marchlinski, A. Mazzanti, L. Mestroni, A. Pantazis, A. Pelliccia, M. P. Marra, K. Pilichou, P. G. A. Platonov, A. Protonotarios, A. Rampazzo, J. E. Saffitz, A. M. Saguner, C. Schmied, S. Sharma, H. Tandri, A. S. J. M. Te Riele, G. Thiene, A. Tsatsopoulou, W. Zareba, A. Zorzi, T. Wichter, F. I. Marcus, H. Calkins, and International Experts. Arrhythmogenic right ventricular cardiomyopathy: evaluation of the current diagnostic criteria and differential diagnosis. Eur. Heart J. 41:1414–1429, 2019.

    Google Scholar 

  18. Cunningham, K. S., D. A. Spears, and M. Care. Evaluation of cardiac hypertrophy in the setting of sudden cardiac death. Forensic Sci. Res. 4:223–240, 2019.

    Google Scholar 

  19. Daoudi, A., S. Mahmoudi, and M. A. Chikh. Automatic segmentation of the left atrium on CT images. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, edited by O. Camara, T. Mansi, M. Pop, K. Rhode, M. Sermesant, and A. Young. Berlin: Springer, 2014, pp. 14–23.

    Google Scholar 

  20. Degel, M. A., N. Navab, and A. Shadi. Domain and geometry agnostic cnns for left atrium segmentation in 3d ultrasound. CoRR, abs/1805.00357, 2018.

  21. Dormer, J. D., L. Ma, M. Halicek, C. M. Reilly, E. Schreibmann, and B. Fei. Heart chamber segmentation from CT using convolutional neural networks. In: Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 10578, edited by B. Gimi, and A. Krol. Washington: International Society for Optics and Photonics, SPIE, 2018, pp. 659–664.

    Google Scholar 

  22. Dorn, G. W., and J. D. Molkentin. Manipulating cardiac contractility in heart failure. Circulation 109:150–158, 2004.

    Google Scholar 

  23. Dou, Q., L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, and P. A. Heng. 3d deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41:40–54, 2017.

    Google Scholar 

  24. Du, X., Y. Susu, R. Tang, Y. Zhang, and S. Li. Cardiac-deepied: automatic pixel-level deep segmentation for cardiac bi-ventricle using improved end-to-end encoder-decoder network. IEEE J. Transl. Eng. Health Med. 7:1–10, 2019.

    Google Scholar 

  25. Du, X., W. Zhang, H. Zhang, J. Chen, Y. Zhang, Warrington J. Claude, G. Brahm, and S. Li. Deep regression segmentation for cardiac bi-ventricle mr images. IEEE Access. 6:3828–3838, 2018.

    Google Scholar 

  26. Duan, J., G. Bello, J. Schlemper, W. Bai, T. J. W. Dawes, C. Biffi, A. de Marvao, G. Doumoud, D. P. O’Regan, and D. Rueckert. Automatic 3d bi-ventricular segmentation of cardiac images by a shape-refined multi- task deep learning approach. IEEE Trans. Med. Imaging 38(9):2151–2164, 2019.

    Google Scholar 

  27. Ecabert, O., J. Peters, H. Schramm, C. Lorenz, J. Berg, M. Walker, M. Vembar, M. Olszewski, K. Subramanyan, G. Lavi, and J. Weese. Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27:1189–1201, 2008.

    Google Scholar 

  28. Ecabert, O., J. Peters, M. Walker, T. Ivanc, C. Lorenz, J. Berg, J. Lessick, M. Vembar, and J. Weese. Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med. Image Anal. 15:63–76, 2011.

    Google Scholar 

  29. Ecabert, O., J. Peters, C. Lorenz, J. von Berg, M. Vembar, K. Subramanyan, G. Lavi, and J. Weese. Towards automatic full heart segmentation in computed-tomography images. In: Computers in Cardiology, pp. 223–226, 2005.

  30. Ferreira, P. F., R. R. Martin, A. D. Scott, Z. Khalique, G. Yang, S. Nielles-Vallespin, D. J. Pennell, and D. N. Firmin. Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation. Magn. Resonan. Med. 2020. https://doi.org/10.1002/mrm.28294.

    Article  Google Scholar 

  31. Frangi, A., W. J. Niessen, and M. Viergever. Three-dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans. Med. Imaging 20:2–25, 2001.

    Google Scholar 

  32. Fritz, D., D. Rinck, R. Dillmann, and M. Scheuering. Segmentation of the left and right cardiac ventricle using a combined bi-temporal statistical model. In: Medical Imaging 2013: Image Processing, Vol. 6141, edited by K. R. Cleary, and R. L. Galloway Jr. Washington: International Society for Optics and Photonics, SPIE, 2006, pp. 605–614.

    Google Scholar 

  33. Fritz, D., D. Rinck, R. Unterhinninghofen, R. Dillmann, and M. Scheuering. Automatic segmentation of the left ventricle and computation of diagnostic parameters using regiongrowing and a statistical model. In: Medical Imaging 2005: Image Processing, edited by J. Michael Fitzpatrick, and J. M. Reinhardt. Washington: SPIE, 2005, pp. 1844–1854.

    Google Scholar 

  34. Galisot, G., T. Brouard, and J. Y. Ramel. Local probabilistic atlases and a posteriori correction for the segmentation of heart images. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, edited by M. Pop, M. Sermesant, P. M. Jodoin, A. Lalande, X. Zhuang, G. Yang, A. Young, and O. Bernard. Cham: Springer, 2018, pp. 207–214.

    Google Scholar 

  35. Geneva: World Health Organization. 2016 (WHO/DAD/2016.1). World health statistics overview 2016: monitoring health for the sdgs, sustainable development goals. https://www.who.int/gho/publications/world_health_statistics/2016/en/. Accessed 16 Sep 2019.

  36. Grosgeorge, D., C. Petitjean, J. Caudron, J. Fares, and J. N. Dacher. Automatic cardiac ventricle segmentation in mr images: a validation study. Int. J. Comput. Assist. Radiol. Surg. 6:573–581, 2011.

    Google Scholar 

  37. Haak, A., G. Vegas Sánchez-Ferrero, H. Mulder, H. Kirisli, N. Baka, C. Metz, S. Klein, B. Ren, G. Burken, J. Pluim, A. van der Steen, T. Walsum, and J. Bosch. Simultaneous segmentation of multiple heart cavities in 3d transesophageal echocardiograms. IEEE International Ultrasonics Symposium, pp. 659–662, 2013.

  38. Haak, A., G. Vegas Sánchez-Ferrero, H.W. Mulder, H. Kirisli, N. Baka, C. Metz, S. Klein, B. Ren, G. Burken, A. Steen, J. Pluim, T. Walsum, and J.G. Bosch.

  39. Habijan, M., H. Leventic, I. Galic, D. Babin. Whole heart segmentation from CT images using 3d u-net architecture. In 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 121–126, 2019.

  40. Haegeli, L. M., and H. Calkins. Catheter ablation of atrial fibrillation: an update. Eur. Heart J. 35(36):2454–2459, 2014.

    Google Scholar 

  41. Hamburger, R. Left ventricular dysfunction in ischemic heart disease. Cardiovasc. Innov. Appl. 3:297–303, 2019.

    Google Scholar 

  42. Heckemann, R., J. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33:115–126, 2006.

    Google Scholar 

  43. Heimann, T., and H. P. Meinzer. Statistical shape models for 3d medical image segmentation: a review. Med. Image Anal. 13:543–563, 2009.

    Google Scholar 

  44. Heinrich, M., and J. Oster. MRI whole heart segmentation using discrete nonlinear registration and fast non-local fusion. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, edited by M. Pop, M. Sermesant, P. M. Jodoin, A. Lalande, X. Zhuang, G. Yang, A. Young, and O. Bernard. Cham: Springer, 2018, pp. 233–241.

    Google Scholar 

  45. Isgum, I., M. Staring, A. Bartels-Rutten, M. Prokop, M. Viergever, and B. Ginneken. Multi-atlas-based segmentation with local decision fusion–application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28:1000–1010, 2009.

    Google Scholar 

  46. Jacobs, S., R. Grunert, F. W. Mohr, and V. Falk. 3d-imaging of cardiac structures using 3d heart models for planning in heart surgery: a preliminary study. Interact. Cardiovasc. Thorac. Surg. 7:6–9, 2008.

    Google Scholar 

  47. Jia, S., A. Despinasse, Z. Wang, H. Delingette, X. Pennec, P. Jais, H. Cochet, and M. Sermesant. Automatically segmenting the left atrium from cardiac images using successive 3d u-nets and a contour loss. abs/1812.02518, 2018.

  48. John, M., and N. Rahn. Automatic left atrium segmentation by cutting the blood pool at narrowings. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2005, edited by J. S. Duncan, and G. Gerig. Berlin: Springer, 2005, pp. 798–805.

    Google Scholar 

  49. Kang, D., J. Woo, P. Slomka, D. Dey, G. Germano, and C. C. J. Kuo. Heart chambers and whole heart segmentation techniques: review. J. Electron. Imaging 21:1–17, 2012.

    Google Scholar 

  50. Kang, H., B. Kim, J. Lee, J. Shin, and Y. G. Shin. Automatic left and right heart segmentation using power watershed and active contour model without edge. Biomed. Eng. Lett. 4:355–361, 2015.

    Google Scholar 

  51. Kara, B., A. Nayman, I. Guler, E. Gul, M. Koplay, and Y. Paksoy. Quantitative assessment of left ventricular function and myocardial mass: a comparison of coronary CT angiography with cardiac MRI and echocardiography. Polish J. Radiol. 81:95–102, 2016.

    Google Scholar 

  52. Karamitsos, T. D., J. M. Francis, S. Myerson, J. B. Selvanayagam, and S. Neubauer. The role of cardiovascular magnetic resonance imaging in heart failure. J. Am. Coll. Cardiol. 54:1407–1424, 2009.

    Google Scholar 

  53. Karim, R., R. Housden, M. Balasubramaniam, Z. Chen, D. Perry, A. Uddin, Y. Al-Beyatti, E. Palkhi, P. Acheampong, S. Obom, A. Hennemuth, Y. Lu, W. Bai, W. Shi, Y. Gao, H. O. Peitgen, P. Radau, R. Razavi, A. Tannenbaum, and K. Rhode. 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. 15:105, 2013.

    Google Scholar 

  54. Karim, R., C. Juli, L. Malcolme-Lawes, D. Wyn-Davies, P. Kanagaratnam, N. Peters, and D. Rueckert. Automatic segmentation of left atrial geometry from contrast-enhanced magnetic resonance images using a probabilistic atlas. In: Statistical Atlases and Computational Models of the Heart, edited by O. Camara, M. Pop, K. Rhode, M. Sermesant, N. Smith, and A. Young. Berlin: Springer, 2010, pp. 134–143.

    Google Scholar 

  55. Kass, M., A. Witkin, and D. Terzopoulos. Snakes: active contour models. Int. J. Comput. Vis. 1:321–331, 1988.

    MATH  Google Scholar 

  56. Katouzian, A., A. Prakash, and E. Konofagou. A new automated technique for left- and right-ventricular segmentation in magnetic resonance imaging. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3074–3077, 2006.

  57. Keane, M. G. Diastolic heart failure. In: Heart Failure: A Clinician’s Guide to Ambulatory Diagnosis and Treatment, pp. 321–337. Humana Press, 2003.

  58. Kirisli, A. H., M. Schaap, S. Klein, L. A. Neefjes, A. C. Weustink, T. van Walsum, and W. J. Niessen. Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach. In: Medical Imaging 2010: Image Processing, Vol. 7623, edited by B. M. Dawant, and D. R. Haynor. Washington: International Society for Optics and Photonics, SPIE, 2010, pp. 55–63.

    Google Scholar 

  59. Kirschner, M., and S. Wesarg. Active shape models unleashed. In: Medical Imaging 2011: Image Processing, Vol. 7962, edited by B. M. Dawant, and D. R. Haynor. Washington: International Society for Optics and Photonics, SPIE, 2011, pp. 329–337.

    Google Scholar 

  60. Kutra, D., A. Saalbach, H. Lehmann, A. Groth, S. P. M. Dries, M. W. Krueger, O. Dössel, and J. Weese. Automatic multi-model-based segmentation of the left atrium in cardiac MRI scans. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2012, edited by N. Ayache, H. Delingette, P. Golland, and K. Mori. Berlin: Springer, 2012, pp. 1–8.

    Google Scholar 

  61. Li, C., C. Xu, C. Gui, and M. D. Fox. Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Med. Imaging 19:3243–3254, 2011.

    MathSciNet  MATH  Google Scholar 

  62. Li, J., R. Zhang, L. Shi, and D. Wang. Automatic whole-heart segmentation in congenital heart disease using deeply-supervised 3d fcn. In: Reconstruction, Segmentation, and Analysis of Medical Images, edited by M. A. Zuluaga, K. Bhatia, B. Kainz, M. H. Moghari, and D. F. Pace. Cham: Springer, 2017, pp. 111–118.

    Google Scholar 

  63. Li, L., F. Wu, G. Yang, L. Xu, T. Wong, R. Mohiaddin, D. Firmin, J. Keegan, and X. Zhuang. Atrial scar quantification via multi-scale cnn in the graph-cuts framework. Med. Image Anal. 60:101595, 2020.

    Google Scholar 

  64. Lieman-Sifry, J., M. Le, F. Lau, S. Sall, and D. Golden. Fastventricle: cardiac segmentation with enet. In: Functional Imaging and Modelling of the Heart, edited by M. Pop, and G. A. Wright. Cham: Springer, 2017, pp. 127–138.

    Google Scholar 

  65. Litjens, G., F. Ciompi, J. Wolterink, B. De Vos, T. Leiner, J. Teuwen, and I. Išgum. State-of-the-art deep learning in cardiovascular image analysis. JACC Cardiovasc. Imaging 12:1549–1565, 2019.

    Google Scholar 

  66. Liu, Y., G. Captur, J. C. Moon, S. Guo, X. Yang, S. Zhang, and C. Li. Distance regularized two level sets for segmentation of left and right ventricles from cine-MRI. Magn. Reson. Imaging 34:699–706, 2016.

    Google Scholar 

  67. Liu, Y., Y. Dai, C. Yan, and K. Wang. Deep learning based method for left atrial segmentation in ge-MRI. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, edited by M. Pop, M. Sermesant, J. Zhao, S. Li, K. McLeod, A. Young, K. Rhode, and T. Mansi. Cham: Springer, 2019, pp. 311–318.

    Google Scholar 

  68. Lung National Heart and Blood Institute. Cardiac atlas project. https://www.cardiacatlas.org/. Accessed 16 June 2020.

  69. Ma, F., J. Liu, B. Wang, and H. Duan. Automatic segmentation of the full heart in cardiac computed tomography images using a haar classifier and a statistical model. J. Med. Imaging Health Inform. 6:1298–1302, 2016.

    Google Scholar 

  70. MacLeod, K. Recent advances in understanding cardiac contractility in health and disease. F1000research 5:1770, 2016.

    Google Scholar 

  71. Mahapatra, D. Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors. J. Digit. Imaging 26:721–730, 2013.

    Google Scholar 

  72. Margeta, J., K. McLeod, A. Criminisi, and N. Ayache. Decision forests for segmentation of the left atrium from 3d MRI. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, edited by O. Camara, T. Mansi, M. Pop, K. Rhode, M. Sermesant, and A. Young. Berlin: Springer, 2014, pp. 49–56.

    Google Scholar 

  73. McCulloch, W., and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 52:99–115, 1990.

    MATH  Google Scholar 

  74. Mitchell, S., B. Lelieveldt, R. van der Geest, H. Bosch, J. Reiber, and M. Sonka. Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac mr images. IEEE Trans. Med. Imaging 20:415–423, 2001.

    Google Scholar 

  75. Mitchell, S. C., J. G. Bosch, B. P. F. Lelieveldt, R. J. van der Geest, J. H. C. Reiber, and M. Sonka. 3-d active appearance models: segmentation of cardiac mr and ultrasound images. IEEE Trans. Med. Imaging 21(9):1167–1178, 2002.

    Google Scholar 

  76. Mortazi, A., J. Burt, and U. Bagci. Multi-planar deep segmentation networks for cardiac substructures from MRI and CT. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2017, edited by M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. L. Collins, and S. Duchesne. Cham: Springer, 2017, pp. 287–295.

    Google Scholar 

  77. Mulder, H., J. Pluim, B. Ren, A. Haak, M. Viergever, J.G. Bosch, and M. van Stralen. Atlas-based mosaicing of 3d transesophageal echocardiography images of the left atrium, pp. 1–4, 2015.

  78. Mulder, H., M. van Stralen, B. Ren, A. Haak, M. Viergever, J. G. Bosch, and J. Pluim. Atlas-based mosaicing of left atrial 3d transesophageal echocardiography images. Ultrasound Med. Biol. 43:765–774, 2017.

    Google Scholar 

  79. Mäkelä, T., P. Clarysse, O. Sipilä, N. Pauna, Q. C. Pham, T. Katila, and I. Magnin. A review of cardiac image registration methods. IEEE Trans. Med. Imaging 21:11–21, 2002.

    Google Scholar 

  80. Nuñez-Garcia, M., X. Zhuang, G. Sanroma, L. Li, L. Xu, C. Butakoff, and O. Camara. Left atrial segmentation combining multi-atlas whole heart labeling and shape-based atlas selection. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, edited by M. Pop, M. Sermesant, J. Zhao, S. Li, K. McLeod, A. Young, K. Rhode, and T. Mansi. Cham: Springer, 2019, pp. 302–310.

    Google Scholar 

  81. Ogunkunle, O., S. Omokhodion, R. Oladokun, and A. A. Odutola. Heart failure complicating tetralogy of fallot. West Afr. J. Med. 23:75–78, 2004.

    Google Scholar 

  82. Oktay, O., E. Ferrante, K. Kamnitsas, M. Heinrich, W. Bai, J. Caballero, R. Guerrero, S. Cook, A. de Marvao, T. Dawes, D. O’Regan, B. Kainz, B. Glocker, and D. Rueckert. Anatomically constrained neural networks (acnn): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37:384–395, 2018.

    Google Scholar 

  83. Oktay, O., W. Shi, K. Keraudren, J. Caballero, and D. Rueckert. Learning shape representations for multi-atlas endocardium segmentation in 3d echo images, 2014.

  84. Ordas, S., L. Boisrobert, M. Huguet, and A. Frangi. Active shape models with invariant optimal features (iof-asm) application to cardiac MRI segmentation. Comput. Cardiol. 30:633–636, 2003.

    Google Scholar 

  85. Ordas, S., E. Oubel, R. Leta, F. Carreras, and A. F. Frangi. A statistical shape model of the heart and its application to model-based segmentation. In: Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, Vol. 6511, edited by A. Manduca, and X. P. Hu. Washington: International Society for Optics and Photonics, SPIE, 2007, pp. 490–500.

    Google Scholar 

  86. Osher, S., and J. A. Sethian. Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations. J. Comput. Phys. 79(1):12–49, 1988.

    MathSciNet  MATH  Google Scholar 

  87. Parmar, B., T. Jarrett, E. Kholmovski, N. Hu, D. Parker, R. MacLeod, N. Marrouche, and R. Ranjan. Poor scar formation after ablation is associated with atrial fibrillation recurrence. J. Interv. Cardiac Electrophysiol. 44:247–256, 2015.

    Google Scholar 

  88. Payer, C., D. Štern, H. Bischof, and M. Urschler. Regressing heatmaps for multiple landmark localization using cnns. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016, edited by S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and G. Wells. Cham: Springer, 2016, pp. 230–238.

    Google Scholar 

  89. Payer, C., D. Štern, H. Bischof, and M. Urschler. Multi-label whole heart segmentation using cnns and anatomical label configurations. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, edited by M. Pop, M. Sermesant, P. M. Jodoin, A. Lalande, X. Zhuang, G. Yang, A. Young, and O. Bernard. Cham: Springer, 2018, pp. 190–198.

    Google Scholar 

  90. Peng, P., K. Lekadir, A. Gooya, L. Shao, S. Petersen, and A. Frangi. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magn. Reson. Mat. Phys. Biol. Med. 29:155–195, 2016.

    Google Scholar 

  91. Peters, J., O. Ecabert, C. Meyer, H. Schramm, R. Kneser, A. Groth, and J. Weese. Automatic whole heart segmentation in static magnetic resonance image volumes. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2007, edited by N. Ayache, S. Ourselin, and A. Maeder. Berlin: Springer, 2007, pp. 402–410.

    Google Scholar 

  92. Petitjean, C., and J. N. Dacher. A review of segmentation methods in short axis cardiac mr images. Med. Image Anal. 15:169–184, 2011.

    Google Scholar 

  93. Ren, S., K. He, R. Girshick, and J. Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2015.

  94. Ronneberger, O., P. Fischer, and T. Brox. U-net: convolutional networks for biomedical image segmentation. LNCS 9351:234–241, 2015.

    Google Scholar 

  95. Rougon, N., C. Petitjean, F. Prêteux, P. Cluzel, and P. Grenier. A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures. Med. Image Anal. 9:353–375, 2005.

    Google Scholar 

  96. Rueckert, D., M. Lorenzo-Valdes, R. Chandrashekara, G.L. Sanchez-Ortiz, and R. Mohiaddin. Non-rigid registration of cardiac mr: application to motion modelling and atlas-based segmentation. In Proceedings IEEE International Symposium on Biomedical Imaging, pp. 481–484, 2002.

  97. Sabuncu, M., B. T. T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. Nonparametric mixture models for supervised image parcellation. Med. Image Comput. Comput. Assist. Interv. 12:301–313, 2009.

    Google Scholar 

  98. Sagastagoitia, J. D., Y. Sáez, M. Vacas, I. Nárvaez, J. P. de Lafuente, E. Molinero, A. Escobar, M. Lafita, and J. A. Iriarte. Acute versus chronic myocardial ischemia: a differential biological profile study. Pathophysiol. Haemost. Thromb. 36(2):91–97, 2007.

    Google Scholar 

  99. Sandoval, Z., J. Betancur, and J. L. Dillenseger. Multi-atlas-based segmentation of the left atrium and pulmonary veins. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, edited by O. Camara, T. Mansi, M. Pop, K. Rhode, M. Sermesant, and A. Young. Berlin: Springer, 2014, pp. 24–30.

    Google Scholar 

  100. Savioli, N., G. Montana, and P. Lamata. V-fcnn: volumetric fully convolution neural network for automatic atrial segmentation, 2018.

  101. Shi, Z., G. Zeng, L. Zhang, X. Zhuang, L. Li, G. Yang, and G. Zheng. Bayesian voxdrn: a probabilistic deep voxelwise dilated residual network for whole heart segmentation from 3d mr images. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2018, edited by A. F. Frangi, J. A. Schnabel, and C. Davatzikos. Cham: Springer, 2018, pp. 569–577.

    Google Scholar 

  102. Sodergren, T., R. Bhalodia, R. Whitaker, J. Cates, N. Marrouche, and S. Elhabian. Mixture modeling of global shape priors and autoencoding local intensity priors for left atrium segmentation. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, edited by M. Pop, M. Sermesant, J. Zhao, S. Li, K. McLeod, A. Young, K. Rhode, and T. Mansi. Cham: Springer, 2019, pp. 357–367.

    Google Scholar 

  103. Soomro, S., F. Akram, A. Munir, C. Lee, and K. Choi. Segmentation of left and right ventricles in cardiac MRI using active contours. Comput. Math. Methods Med. 1–16:2017, 2017.

    MathSciNet  MATH  Google Scholar 

  104. STACOM. Statistical atlases and computational modelling of the heart. http://stacom.cardiacatlas.org/. Accessed: 10 June 2020.

  105. Stefanadis, C., J. Dernellis, and P. Toutouzas. A clinical appraisal of left atrial function. Eur. Heart J. 22:22–36, 2001.

    Google Scholar 

  106. Stender, B., O. Blanck, B. Wang, and A. Schlaefer. Model-based segmentation of the left atrium in CT and MRI scans. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, edited by O. Camara, T. Mansi, M. Pop, K. Rhode, M. Sermesant, and A. Young. Berlin: Springer, 2014, pp. 31–41.

    Google Scholar 

  107. Sultana, R., N. Sultana, A. Rashid, S. Z. Rasheed, M. Ahmed, M. Ishaq, and A. Samad. Cardiac arrhythmias and left ventricular hypertrophy in systemic hypertension. J. Ayub. Med. Coll. Abbottabad 22(4):155–158, 2010.

    Google Scholar 

  108. Tavakoli, V., and A. A. Amini. A survey of shaped-based registration and segmentation techniques for cardiac images. Comput. Vis. Image Underst. 117:966–989, 2013.

    Google Scholar 

  109. Tong, Q., N. Munan, S. Weixin, L. Xiangyun, and Q. Ying. 3d deeply-supervised u-net based whole heart segmentation. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, edited by M. Pop, M. Sermesant, P. M. Jodoin, A. Lalande, X. Zhuang, G. Yang, A. Young, and O. Bernard. Cham: Springer, 2018, pp. 224–232.

    Google Scholar 

  110. Turco, D., C. Corsi, and C. Lamberti. Fully automated quantification of left and right ventricular volumes throughout the cardiac cycle from MRI. Comput. Cardiol. 38:377–380, 2011.

    Google Scholar 

  111. Turco, D., C. Corsi, and C. Lamberti. Fully automated quantification of left and right ventricular volumes throughout the cardiac cycle from magnetic resonance imaging. In 2011 Computing in Cardiology, pp. 377–380, 2011.

  112. Valverde, I., G. Gomez, A. Gonzalez, C. Suarez-Mejias, A. Adsuar, J. F. Coserria, S. Uribe, T. Gomez-Cia, and A. R. Hosseinpour. Three-dimensional patient-specific cardiac model for surgical planning in nikaidoh procedure. Cardiol. Young 25:698–704, 2015.

    Google Scholar 

  113. van Rikxoort, E., I. Isgum, Y. Arzhaeva, M. Staring, S. Klein, M. Viergever, J. Pluim, and B. Ginneken. Adaptive local multi-atlas segmentation: application to the heart and the caudate nucleus. Med. Image Anal. 14:39–49, 2009.

    Google Scholar 

  114. Wang, C., T. MacGillivray, G. Macnaught, G. Yang, and D. Newby. A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data. STACOM@MICCAI, 2018.

  115. Wang, C., T. MacGillivray, G. Macnaught, G. Yang, and D. Newby. A two-stage u-net model for 3d multi-class segmentation on full-resolution cardiac data. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, edited by M. Pop, M. Sermesant, J. Zhao, S. Li, K. McLeod, A. Young, K. Rhode, and T. Mansi. Cham: Springer, 2019, pp. 191–199.

    Google Scholar 

  116. Wang, C., and O. Smedby. Automatic whole heart segmentation using deep learning and shape context. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, edited by M. Pop, M. Sermesant, P. M. Jodoin, A. Lalande, X. Zhuang, G. Yang, A. Young, and O. Bernard. Cham: Springer, 2018, pp. 242–249.

    Google Scholar 

  117. Wang, L., K. Lekadir, R. Merrifield, S. L. Lee, and G. Z. Yang. A general framework for context-specific image segmentation using reinforcement learning. IEEE Trans. Med. Imaging 32:943–956, 2013.

    Google Scholar 

  118. Xia, Q., Y. Yao, Z. Hu, and A. Hao. Automatic 3d atrial segmentation from ge-MRIs using volumetric fully convolutional networks. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, edited by M. Pop, M. Sermesant, J. Zhao, S. Li, K. McLeod, A. Young, K. Rhode, and T. Mansi. Cham: Springer, 2019, pp. 211–220.

    Google Scholar 

  119. Xiong, Z., V. V. Fedorov, X. Fu, E. Cheng, R. Macleod, and J. Zhao. Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans. Med. Imaging 38(2):515–524, 2019.

    Google Scholar 

  120. Xu, C., and J. L. Prince. Snakes, shapes, and gradient vector flow. IEEE Trans. Med. Imaging 7:359–369, 1998.

    MathSciNet  MATH  Google Scholar 

  121. Xu, Z., Z. Wu, and J. Feng. Cfun: combining faster r-cnn and u-net network for efficient whole heart segmentation. CoRR, abs/1812.04914, 2018.

  122. Yang, G., J. Chen, Z. Gao, S. Li, H. Ni, E. Angelini, T. Wong, R. Mohiaddin, E. Nyktari, R. Wage, and L. Xu. Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. Future Gen. Comput. Syst. 107:215–228, 2020.

    Google Scholar 

  123. Yang, G., J. Chen, Z. Gao, H. Zhang, H. Ni, E. Angelini, R. Mohiaddin, T. Wong, J. Keegan, and D. Firmin. Multiview sequential learning and dilated residual learning for a fully automatic delineation of the left atrium and pulmonary veins from late gadolinium-enhanced cardiac MRI images, vol. 2018, pp 1123–1127, 2018.

  124. Yang, G., C. Sun, Y. Chen, L. Tang, H. Shu, and J. L. Dillenseger. Automatic whole heart segmentation in CT images based on multi-atlas image registration. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, edited by M. Pop, M. Sermesant, P. M. Jodoin, A. Lalande, X. Zhuang, G. Yang, A. Young, and O. Bernard. Cham: Springer, 2018, pp. 250–257.

    Google Scholar 

  125. Yang, G., X. Zhuang, H. Khan, S. Haldar, E. Nyktari, L. Li, R. Wage, X. Ye, G. Slabaugh, T. Wong, J. Keegan, and D. Firmin. Fully automatic segmentation and objective assessment of atrial scars for longstanding persistent atrial fibrillation patients using late gadolinium-enhanced MRI. Med. Phys. 45(4):1562–1576, 2017.

    Google Scholar 

  126. Yang, G., X. Zhuang, H. Khan, S. Haldar, E. Nyktari, L. Li, X. Ye, G. Slabaugh, T. Wong, R. Mohiaddin, J. Keegan, and D. Firmin. Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI. In: Medical Imaging 2017: Image Processing, Vol. 10133, edited by M. A. Styner, and E. D. Angelini. Washington: International Society for Optics and Photonics, SPIE, 2017, pp. 312–318.

    Google Scholar 

  127. Yang, G., X. Zhuang, H. Khan, S. Haldar, E. Nyktari, X. Ye, G. Slabaugh, T. Wong, R. Mohiaddin, J. Keegan, and D. Firmin. A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 844–848, 2017.

  128. Yang, X., C. Bian, L. Yu, D. Ni, and P. A. Heng. 3d convolutional networks for fully automatic fine-grained whole heart partition. In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, edited by M. Pop, M. Sermant, P. M. Jodoin, A. Lalande, X. Zhuang, G. Yang, A. Young, and O. Bernard. Cham: Springer, 2018, pp. 181–189.

    Google Scholar 

  129. Yang, X., N. Wang, Y. Wang, X. Wang, R. Nezafat, D. Ni, and P.A. Heng. Combating uncertainty with novel losses for automatic left atrium segmentation. CoRR, abs/1812.05807, 2018.

  130. Ye, C., W. Wang, S. Zhang, and K. Wang. Multi-depth fusion network for whole-heart CT image segmentation. IEEE Access. 7:23421–23429, 2019.

    Google Scholar 

  131. Yi, C. H., S. D. Jeong, and J. Cho. Balloon-like active contour model using variable closet points. J. Korea Acad. Ind. Coop. Soc. 13:3654–3659, 2012.

    Google Scholar 

  132. Yu, L., J. Z. Cheng, Q. Dou, X. Yang, H. Chen, J. Qin, and P. A. Heng. Automatic 3d cardiovascular mr segmentation with densely-connected volumetric convnets. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2017, edited by M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. L. Collins, and S. Duchesne. Cham: Springer, 2017, pp. 287–295.

    Google Scholar 

  133. Zagrodsky, V., V. Walimbe, C. Castro-Pareja, J. Qin, J. M. Song, and R. Shekhar. Registration-assisted segmentation of real-time 3-d echocardiographic data using deformable models. IEEE Trans. Med. Imaging 24:1089–1099, 2005.

    Google Scholar 

  134. Zhang, H., A. Wahle, R. Johnson, T. Scholz, and M. Sonka. 4-d cardiac mr image analysis: left and right ventricular morphology and function. IEEE Trans. Med. Imaging 29:350–364, 2009.

    Google Scholar 

  135. Zhao, X., Y. Wang, and G. Jozsef. Robust shape-constrained active contour for whole heart segmentation in 3-d CT images for radiotherapy planning. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 1–5, 2014.

  136. Zheng, Y. Model-Based 3D Cardiac Image Segmentation With Marginal Space Learning. New York: Academic Press, pp. 383–403, 2016.

    Google Scholar 

  137. Zheng, Y., A. Barbu, B. Georgescu, M. Scheuering, and D. Comaniciu. Fast automatic heart chamber segmentation from 3d CT data using marginal space learning and steerable features. ICCV, pp. 1–8, 2007.

  138. Zheng, Y., A. Barbu, B. Georgescu, M. Scheuering, and D. Comaniciu. Four-chamber heart modeling and automatic segmentation for 3-d cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imaging 27:1668–1681, 2008.

    Google Scholar 

  139. Zheng, Y., and D. Comaniciu. Marginal Space Learning for Medical Image Analysis: Efficient Detection and Segmentation of Anatomical Structures. New York: Springer, pp. 1–268, 2014.

    MATH  Google Scholar 

  140. Zheng, Y., B. Georgescu, H. Ling, S.K. Zhou, M. Scheuering, and D. Comaniciu. Constrained marginal space learning for efficient 3d anatomical structure detection in medical images. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 194–201, 2009.

  141. Zhu, L., Y. Gao, A. Yezzi, and A. Tannenbaum. Automatic segmentation of the left atrium from mr images via variational region growing with a moments-based shape prior. IEEE Trans. Image Process. 22:5111–5122, 2013.

    MathSciNet  MATH  Google Scholar 

  142. Zhuang, X. Challenges and methodologies of fully automatic whole heart segmentation: a review. J. Healthc. Eng. 4:371–408, 2013.

    Google Scholar 

  143. Zhuang, X., W. Bai, J. Song, S. Zhan, Q. Xiaohua, W. Shi, Y. Lian, and D. Rueckert. Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Med. Phys. 42:3822–3833, 2015.

    Google Scholar 

  144. Zhuang, X., L. Li, C. Payer, D. Štern, M. Urschler, M. P. Heinrich, J. Oster, C. Wang, O. Smedby, C. Bian, X. Yang, P. A. Heng, A. Mortazi, U. Bagci, G. Yang, C. Sun, G. Galisot, J. Y. Ramel, T. Brouard, O. Tong, W. Si, X. Liao, G. Zeng, Z. Shi, G. Zheng, C. Wang, T. MacGillivray, D. Newby, K. Rhode, S. Ourselin, R. Mohiaddin, J. Keegan, D. Firmin, and G. Yang. Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med. Image Anal. 58:101–537, 2019.

    Google Scholar 

  145. Zhuang, X., K. Rhode, S. Arridge, R. Razavi, D. Hill, D. Hawkes, and S. Ourselin. An atlas-based segmentation propagation framework using locally affine registration—application to automatic whole heart segmentation. Med. Image Comput. Comput. Assist. Interv. 52425:425–433, 2008.

    Google Scholar 

  146. Zhuang, X., K. Rhode, R. Razavi, D. Hawkes, and S. Ourselin. A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imaging 29:1612–1625, 2010.

    Google Scholar 

  147. Zhuang, X., J. Song, S. Zhan, T. Lan, H. Huang, M. Hu, S. Ourselin, and Q. Li. A registration and atlas propagation based framework for automatic whole heart segmentation of CT volumes. In: Medical Imaging 2013: Image Processing, Vol. 8669, edited by S. Ourselin, and D. R. Haynor. Washington: International Society for Optics and Photonics, SPIE, 2013, pp. 1076–1083.

    Google Scholar 

  148. Zhuang, X., C. Yao, Y. Ma, D. Hawkes, G. Penney, and S. Ourselin. Registration-based propagation for whole heart segmentation from compounded 3d echocardiography. In: 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings, pp. 1093–1096, 2010.

  149. Zuluaga, A. M., M. J. Cardoso, M. Modat, and S. Ourselin. Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion. In: Functional Imaging and Modeling of the Heart, edited by S. Ourselin, D. Rueckert, and N. Smith. Berlin: Springer, 2013, pp. 174–181.

    Google Scholar 

Download references

Acknowledgments

This work has been supported in part by Croatian Science Foundation under the Project UIP-2017-05-4968.

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical Statement

No human studies were carried out by the authors for this article. No animal studies were carried out by the authors for this article.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Kneza Trpimira 2b, 31000, Osijek, Croatia

    Marija Habijan, Irena Galić, Hrvoje Leventić & Krešimir Romić

  2. Faculty of Engineering and Architecture, imec-TELIN-IPI, imec-Ghent University, Gent, Belgium

    Danilo Babin

  3. Faculty of Medicine, Institute for Cardiovascular Diseases of Vojvodina, University of Novi Sad, Serbia, Sremska Kamenica, Serbia

    Lazar Velicki

  4. TELIN-GAIM, Faculty of Engineering and Architecture, Ghent University, Gent, Belgium

    Aleksandra Pižurica

Authors
  1. Marija Habijan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danilo Babin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Irena Galić
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hrvoje Leventić
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Krešimir Romić
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lazar Velicki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aleksandra Pižurica
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marija Habijan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Habijan, M., Babin, D., Galić, I. et al. Overview of the Whole Heart and Heart Chamber Segmentation Methods. Cardiovasc Eng Tech 11, 725–747 (2020). https://doi.org/10.1007/s13239-020-00494-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13239-020-00494-8

Keywords

Search

Navigation