http://iet.metastore.ingenta.com
1887

Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets

Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In cardiac imaging, the boundary of the left ventricle (LV) could be used to measure the dyssynchrony of the heart. Hence, automatic and optimal segmentation of the LV is important. Although deep learning-based methods have achieved significant break-throughs in the accuracy of segmenting LV, it relies on a great number of training sets and the reproduction quality of the tested cases. Due to the variety of patients, it is difficult or impossible to collect the complete training sets that cover all patients with different genders, races, and ages. Therefore, methods independent of the training sets are more reliable and efficient for clinical applications. In this study, the authors propose a training sets-independent method to segment LV optimally and it outperforms all available state-of-the-art training-sets-independent image segmentation methods. In addition, they propose a framework to identify the boundary of the LV automatically. They tested these segmentation methods with both good quality and poor quality images in the proposed framework and verified that the proposed segmentation method yields the optimal solution compared to other state-of-the-art training-sets-independent segmentation methods. Based on their previous research work, the identified boundaries by the proposed approach are accurate enough for calculating the dyssynchrony of the LV.

References

    1. 1)
      • 1. Petitjean, C., Dacher, J.N.: ‘A review of segmentation methods in short axis cardiac MR images’, Med. Image Anal.., 2011, 15, (2), pp. 169184.
    2. 2)
      • 2. Wang, Z.Z.: ‘An efficient and robust method for automatically identifying the left ventricular boundary in cine magnetic resonance images’, IEEE T-ASE, 2016, 13, (2), pp. 536542.
    3. 3)
      • 3. Chow, C.K., Kaneko, T.: ‘Automatic boundary detection of the left ventricle from cineangiograms’, Comput. Biomed. Res., 1972, 5, pp. 388410.
    4. 4)
      • 4. Brink, A.D., Pendock, N.E.: ‘Minimum cross-entropy threshold selection’, Pattern Recogn., 1996, 29, pp. 179188.
    5. 5)
      • 5. Shanbag, A.G.: ‘Utilization of information measure as a means of image thresholding’, Comput. Vis. Graph. Image Process., 1994, 56, pp. 414419.
    6. 6)
      • 6. Ridler, T.W., Calvard, S.: ‘Picture thresholding using an iterative selection method’, IEEE Trans. Syst. Man Cybern., 1978, SMC-8, pp. 630632.
    7. 7)
      • 7. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: ‘A new method for gray-level picture thresholding using the entropy of the histogram’, Comput. Vis. Graph. Image Process., 1985, 29, pp. 273285.
    8. 8)
      • 8. Aja-Fernández, S., Curiale, A.H., Vegas-Sánchez-Ferrero, G.: ‘A local fuzzy thresholding methodology for multi-region image segmentation’, Knowl.-Based. Syst., 2015, 83, (C), pp. 112.
    9. 9)
      • 9. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybern., 1979, 9, (1), pp. 6266.
    10. 10)
      • 10. Wang, Z.Z., Xiong, J.J., Yang, Y.M., et al: ‘A flexible and robust threshold selection method’, IEEE Trans. Circuits Syst. Video Technol., 2018, 28, (9), pp. 22202232.
    11. 11)
      • 11. Dempster, A.P., Laird, N.M., Rubin, D.B.: ‘Maximum likelihood from incomplete data via the EM algorithm’, J. Royal Statist. Soc. Series B, 1977, 39, (1), pp. 138.
    12. 12)
      • 12. Forgy, E.W.: ‘Cluster analysis of multivariate data: efficiency versus interpretability of classifications’, Biometrics., 1965, 21, (3), pp. 768769.
    13. 13)
      • 13. Dunn, J.C.: ‘A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters’, J. Cybern., 1973, 3, (3), pp. 3257.
    14. 14)
      • 14. Chan, T.F., Esedoglu, S., Nikolova, M.: ‘Algorithms for finding global minimizers of image segmentation and denoising models’, SIAM J. App. Math., 2006, 66, (5), pp. 16321648.
    15. 15)
      • 15. Li, C., Xu, C., Gui, C., et al: ‘Distance regularized level set evolution and its application to image segmentation’, IEEE Trans. Image Process., 2010, 19, (12), pp. 32433254.
    16. 16)
      • 16. Chan, T.F., Vese, L.A.: ‘Active contours without edges’, IEEE Trans. Image Process., 2001, 10, (2), pp. 266277.
    17. 17)
      • 17. Lankton, S., Tannenbaum, A.: ‘Localizing region-based active contours’, IEEE Trans. Image Process., 2008, 17, (11), pp. 20292039.
    18. 18)
      • 18. Wang, Z.Z., Li, H.X.: ‘Generalizing cell segmentation and quantification’, BMC bioinformatics, 2017, 18, (1), pp. 189205.
    19. 19)
      • 19. Radau, P., Lu, Y., Paul, K.G., et al: ‘Evaluation framework for algorithms segmenting short axis cardiac MRI’, MIDAS J.-Card. MR Left Ventricle Segmentation Chall., 2009, https://www.midasjournal.org/browse/publication/658.
    20. 20)
      • 20. Ngo, T.A., Lu, Z., Carneiro, G.: ‘Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance’, Med. Image Anal., 2017, 35, pp. 159171.
    21. 21)
      • 21. Tan, L.K., Liew, Y.M., Lim, E., et al: ‘Cardiac left ventricle segmentation using convolutional neural network regression’. 2016 IEEE EMBS Conf. on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, 2016, pp. 490493.
    22. 22)
      • 22. Arif, O., Sundaramoorthi, G., Hong, B.W., et al: ‘Tracking using motion estimation with physically motivated inter-region constraints’, IEEE Trans. Med. Imaging, 2014, 33, (9), pp. 18751889.
    23. 23)
      • 23. Constantinidès, C., Roullot, E., Lefort, M., et al: ‘Fully automated segmentation of the left ventricle applied to cine MR images: description and results on a database of 45 subjects’. 2012 Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, 2012, pp. 32073210.
    24. 24)
      • 24. Liu, Y., Xue, H., Guetter, C., et al: ‘Moving propagation of suspicious myocardial infarction from delayed enhanced cardiac imaging to CINE MRI using hybrid image registration’. 2011 IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, Chicago, IL, 2011, pp. 12841288.
    25. 25)
      • 25. Schaerer, J., Rouchdy, Y., Clarysse, P., et al: ‘Simultaneous segmentation of the left and right heart ventricles in 3D cine MR images of small animals’. Computers in Cardiology, 2005, Lyon, 2005, pp. 231234.
    26. 26)
      • 26. Jolly, M.P., Alvino, C., Odry, B., et al: ‘Automatic femur segmentation and condyle line detection in 3D MR scans for alignment of high resolution MR’. IEEE Int. Symp. on Biomedical Imaging: From Nano to Macro, Rotterdam, 2010, pp. 940943.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5878
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5878
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address