Skip to main content

Advertisement

SpringerLink
Log in

Real-time aortic valve segmentation from transesophageal echocardiography sequence

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

 Geometric features of the aortic valve play an important role in many applications, such as the clinical diagnostics, shape modeling and image-guided cardiac interventions, especially for the transcatheter aortic valve implantation (TAVI) procedure. However, few works have been reported on the topic of aortic valve segmentation from transesophageal echocardiography (TEE) sequences. To obtain accurate segmentation results and further provide valid support for TAVI, this paper presents a real-time method for segmenting the aortic valve from intraoperative, short-axis view TEE sequences, using an improved probability estimation and continuous max-flow (CMF) approach.

Methods

The proposed segmentation method includes two key stages: (1) In the probability estimation stage, five different prior frames spanning a cardiac circle are firstly selected with the aortic valve manually segmented by an expert. Then, the improved composite probability estimation (CPE) and single probability estimation (SPE) over the five prior frames are, respectively, constructed based on their radial average intensity and radial distance. (2) In the energy function construction stage, the similarity metric is calculated to find out the matching exponents between the current input TEE frame and the prior frames. The typical foreground and background intensities of prior images are therefore used to construct the corresponding energy function. Finally, the CMF approach, accelerated with a graphic processing unit (GPU), is employed to achieve the aortic valve contours in real time.

Results

The evaluation study contained 30 sequences, with each containing 62–146 short-axis TEE frames. The results were compared with the manual segmentation (ground truth). The average symmetric contour distance (ASCD), dice metric (DM) and the reliability of the algorithm reached 0.85 \(\pm \) 0.21 mm, 0.96 \(\pm \) 0.01 and 0.90 (\(d=0.95\)), respectively, and the computation time was 57.04 \(\pm \) 8.98 ms per frame.

Conclusion

The experiment results reveal that the proposed method can achieve accurate and real-time segmentation of aortic valve from TEE sequence of short-axis view.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M (2006) Burden of valvular heart diseases: a population-based study. Lancet 368(9540):1005–1011

    Article  PubMed  Google Scholar 

  2. Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, Tuzcu EM, Webb JG, Fontana GP, Makkar RR (2010) Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med 363(17):1597–1607

    Article  CAS  PubMed  Google Scholar 

  3. Luo Z, Cai J, Gu L (2013) A pilot study on magnetic navigation for transcatheter aortic valve implantation using dynamic aortic model and US image guidance. Int J Comput Assist Radiol Surg 8(4):677–690

    Article  PubMed  Google Scholar 

  4. Masuda K, Kimura E, Tateishi N, Ishihara K (2001) Three dimensional motion mechanism of ultrasound probe and its application for tele-echography system. In: Proceedings. 2001 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 1112–1116

  5. Huber CH, Nasratulla M, Augstburger M, Von Segesser LK (2004) Ultrasound navigation through the heart for off-pump aortic valved stent implantation: new tools for new goals. J Endovasc Ther 11(4):503–510

    Article  PubMed  Google Scholar 

  6. Schneider RJ, Perrin DP, Vasilyev NV, Marx GR, del Nido PJ, Howe RD (2010) Mitral annulus segmentation from 3D ultrasound using graph cuts. IEEE Trans Med Imaging 29(9):1676–1687

    Article  PubMed Central  PubMed  Google Scholar 

  7. Wolf I, Hastenteufel M, De Simone R, Vetter M, Glombitza G, Mottl-Link S, Vahl C-F, Meinzer H-P (2002) ROPES: A semiautomated segmentation method for accelerated analysis of three-dimensional echocardiographic data. IEEE Trans Med Imaging 21(9):1091–1104

    Article  PubMed  Google Scholar 

  8. Luo Z, Cai J, Peters TM, Gu L (2013) Intra-operative 2-D ultrasound and dynamic 3-D aortic model registration for magnetic navigation of transcatheter aortic valve implantation. IEEE Trans Med Imaging 32(11):2152–2165

    Article  PubMed  Google Scholar 

  9. Lang P, Rajchl M, McLeod AJ, Chu MW, Peters TM (2012) Feature identification for image-guided transcatheter aortic valve implantation. In: SPIE Medical Imaging. International Society for Optics and Photonics, pp 83162X–83162X-83114

  10. Dias JM, Leitao JM (1996) Wall position and thickness estimation from sequences of echocardiographic images. IEEE Trans Med Imaging 15(1):25–38

    Article  CAS  PubMed  Google Scholar 

  11. Chen Y, Huang F, Tagare HD, Rao M (2007) A coupled minimization problem for medical image segmentation with priors. Int J Comput Vis 71(3):259–272

    Article  Google Scholar 

  12. Carneiro G, Nascimento J, Freitas A (2010) Robust left ventricle segmentation from ultrasound data using deep neural networks and efficient search methods. In: 2010 IEEE international symposium on biomedical imaging: from nano to macro. IEEE, pp 1085–1088

  13. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137

    Article  PubMed  Google Scholar 

  14. Rajchl M, Yuan J, Ukwatta E, Peters P (2012) Fast interactive multi-region cardiac segmentation with linearly ordered labels. In: 2012 9th IEEE international symposium on biomedical imaging (ISBI). IEEE, pp 1409–1412

  15. Yuan J, Bae E, Tai X-C, Boykov Y (2010) A continuous max-flow approach to potts model. In: Daniilidis K, Maragos P, Paragios N (eds) Computer Vision-ECCV 2010. Springer, Berlin, pp 379–392

  16. Yuan J, Bae E, Tai X-C (2010) A study on continuous max-flow and min-cut approaches. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2217–2224

  17. Boykov YY, Jolly M-P (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: Proceedings. Eighth IEEE international conference on computer vision. ICCV 2001. IEEE, pp 105–112

  18. Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987–1010

    Article  PubMed  Google Scholar 

  19. Lang P, Rajchl M, Li F, Peters TM (2011) Towards model-enhanced real-time ultrasound guided cardiac interventions. In: 2011 International conference on intelligent computation and bio-medical instrumentation (ICBMI). IEEE, pp 89–92

  20. Nie Y, Luo Z, Cai J, Gu L (2013) A novel aortic valve segmentation from ultrasound image using continuous max-flow approach. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 3311–3314

  21. Hatt M, Cheze le Rest C (2009) A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging 28(6):881–893

  22. Kwak N, Choi C-H (2002) Input feature selection by mutual information based on Parzen window. IEEE Trans Pattern Anal Mach Intell 24(12):1667–1671

    Article  Google Scholar 

  23. Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265

  24. Ben Ayed I, H-m Chen (2012) Max-flow segmentation of the left ventricle by recovering subject-specific distributions via a bound of the Bhattacharyya measure. Med Image Anal 16(1):87–100

    Article  PubMed  Google Scholar 

  25. Arya S, Mount DM, Netanyahu NS, Silverman R, Wu A (1994) An optimal algorithm for approximate nearest neighbor searching. In: Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, Philadelphia, pp 573–582

Download references

Acknowledgments

This research is partially supported by the Chinese NSFC research fund (81301283, 61190120, 61190124, 61271318), SRF for ROCS, SEM, the Shanghai municipal health bureau research fund (2011216) and Biomedical engineering fund of Shanghai Jiao Tong University (YG2012MS21).

Conflict of interest

Lixu Gu, Junfeng Cai, Xiahai Zhuang, Yuanyuan Nie and Zhe Luo declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Cardiosurgery, Ruijin Hospital, Shanghai, China

    Junfeng Cai

  2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xiahai Zhuang

  3. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yuanyuan Nie, Zhe Luo & Lixu Gu

Authors
  1. Junfeng Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiahai Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanyuan Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhe Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lixu Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lixu Gu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, J., Zhuang, X., Nie, Y. et al. Real-time aortic valve segmentation from transesophageal echocardiography sequence. Int J CARS 10, 447–458 (2015). https://doi.org/10.1007/s11548-014-1104-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-014-1104-y

Keywords

Search

Navigation