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

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.

Inspec keywords: biomedical MRI; cardiology; image segmentation; learning (artificial intelligence); medical image processing

Other keywords: segmentation method; state-of-the-art training-sets-independent segmentation methods; optimal segmentation; deep learning-based methods; significant break-throughs; cardiac imaging; complete training sets; cardiac magnetic resonance images; poor quality images; optimal solution; left ventricle; available state-of-the-art training-sets-independent image segmentation methods; segment LV optimally; training sets-independent method

Subjects: Biology and medical computing; Patient diagnostic methods and instrumentation; Computer vision and image processing techniques; Optical, image and video signal processing; Biomedical magnetic resonance imaging and spectroscopy; Knowledge engineering techniques; Medical magnetic resonance imaging and spectroscopy

References

    1. 1)
      • 12. Forgy, E.W.: ‘Cluster analysis of multivariate data: efficiency versus interpretability of classifications’, Biometrics., 1965, 21, (3), pp. 768769.
    2. 2)
      • 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.
    3. 3)
      • 16. Chan, T.F., Vese, L.A.: ‘Active contours without edges’, IEEE Trans. Image Process., 2001, 10, (2), pp. 266277.
    4. 4)
      • 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.
    5. 5)
      • 6. Ridler, T.W., Calvard, S.: ‘Picture thresholding using an iterative selection method’, IEEE Trans. Syst. Man Cybern., 1978, SMC-8, pp. 630632.
    6. 6)
      • 17. Lankton, S., Tannenbaum, A.: ‘Localizing region-based active contours’, IEEE Trans. Image Process., 2008, 17, (11), pp. 20292039.
    7. 7)
      • 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.
    8. 8)
      • 4. Brink, A.D., Pendock, N.E.: ‘Minimum cross-entropy threshold selection’, Pattern Recogn., 1996, 29, pp. 179188.
    9. 9)
      • 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.
    10. 10)
      • 5. Shanbag, A.G.: ‘Utilization of information measure as a means of image thresholding’, Comput. Vis. Graph. Image Process., 1994, 56, pp. 414419.
    11. 11)
      • 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.
    12. 12)
      • 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.
    13. 13)
      • 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.
    14. 14)
      • 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.
    15. 15)
      • 9. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybern., 1979, 9, (1), pp. 6266.
    16. 16)
      • 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.
    17. 17)
      • 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.
    18. 18)
      • 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.
    19. 19)
      • 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.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 18. Wang, Z.Z., Li, H.X.: ‘Generalizing cell segmentation and quantification’, BMC bioinformatics, 2017, 18, (1), pp. 189205.
    23. 23)
      • 3. Chow, C.K., Kaneko, T.: ‘Automatic boundary detection of the left ventricle from cineangiograms’, Comput. Biomed. Res., 1972, 5, pp. 388410.
    24. 24)
      • 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.
    25. 25)
      • 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.
    26. 26)
      • 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.
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