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Segmentation of the left ventricle in short-axis sequences by combining deformation flow and optical flow

Segmentation of the left ventricle in short-axis sequences by combining deformation flow and optical flow

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To help the clinicians to segment the borders of the left ventricle (LV) efficiently during measurement of the heart, the authors come up with a semi-automatic approach in this study that is capable of identifying the endocardial borders robustly from cine magnetic resonance images. Firstly, the deformation flow is computed between the inputted boundary in the previous frame and the extracted edge of the LV in the current frame based on boundary minimum distance principle (BMDP). Then, the deformation flow is constrained by optical flow calculated by a partial differential equation model. A smooth deformation boundary is then formed by minimising the energy between the previously inputted boundary and the rough boundary obtained by BMDP and optical flow constraint. To extract edge of the LV as accurate as possible, a threshold selection method is used and improved based on the previous study. The proposed approach is tested on the open access dataset. The computed average perpendicular distance is 1.36 ± 0.24 mm and the computed Dice measure is 90.7% ± 0.15%. Experimental results show that the proposed approach is significantly more accurate than the referenced state of art methods.

References

    1. 1)
      • 1. Radau, P., Lu, Y., Connelly, K., et al: ‘Evaluation framework for algorithms segmenting short axis cardiac MRI’, MIDAS J., 2009, http://hdl.handle.net/10380/3070.
    2. 2)
      • 2. Lee, H.Y., Codella, N.C., Cham, M.D., et al: ‘Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI’, IEEE Trans. Biomed. Eng., 2010, 57, (4), pp. 905913.
    3. 3)
      • 3. Kurkure, U., Pednekar, A., Muthupillai, R., et al: ‘Localization and segmentation of left ventricle in cardiac cine-MR images’, IEEE Trans. Biomed. Eng., 2009, 56, (5), pp. 13601370.
    4. 4)
      • 4. Jolly, M.P.: ‘Automatic segmentation of the left ventricle in cardiac MR and CT images’, Int. J. Comput. Vis., 2006, 70, (2), pp. 151163.
    5. 5)
      • 5. Cocosco, C.A., Niessen, W.J., Netsch, T., et al: ‘Automatic image-driven segmentation of the ventricles in cardiac cine MRI’, J. Magn. Reson. Imaging, 2008, 28, (2), pp. 366374.
    6. 6)
      • 6. Grosgeorge, D., Petitjean, C., Caudron, J., et al: ‘Automatic cardiac ventricle segmentation in MR images: a validation study’, Int. J. Comput. Assist. Radiol. Surg., 2008, 6, (5), pp. 573581.
    7. 7)
      • 7. Andreopoulos, A., Tsotsos, J.K.: ‘Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI’, Med. Image Anal., 2008, 12, (3), pp. 335357.
    8. 8)
      • 8. Zhang, H., Wahle, A., Johnson, R.K., et al: ‘4-D cardiac MR image analysis: left and right ventricular morphology and function’, IEEE Trans. Med. Imaging, 2010, 29, (2), pp. 350364.
    9. 9)
      • 9. Van der Geest, R.J., Boudewijn Lelieveldt, R.J., Angelie, E., et al: ‘Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an active appearance motion model’, J. Cardiovasc. Magn. Reson., 2004, 6, (3), pp. 609617.
    10. 10)
      • 10. Zhuang, X., Rhode, K., Razavi, R., et al: ‘A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI’, IEEE Trans. Med. Imaging, 2010, 29, (9), pp. 16121625.
    11. 11)
      • 11. Dong, D., Sun, Y., Ong, S.H., et al: ‘Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long and short axis information’, Med. Image Anal., 2013, 17, (6), pp. 685697.
    12. 12)
      • 12. Peters, J., Ecabert, O., Meyer, C., et al: ‘Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation’, Med. Image Anal., 2010, 14, (1), pp. 7084.
    13. 13)
      • 13. Bistoquet, A., Oshinski, J., Skrinjar, O.: ‘Myocardial deformation recovery from cine MRI using a nearly incompressible biventricular model’, Med. Image Anal., 2008, 12, (1), pp. 6985.
    14. 14)
      • 14. 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.
    15. 15)
      • 15. Ben Ayed, I., Lu, Y., Li, S.: ‘Left ventricle tracking using overlap priors’, Med. Image Comput. Comput. Assist. Interv. Int. Conf. , 2008, 11, (1), pp. 10251033.
    16. 16)
      • 16. Punithakumar, K., Ben, I., Islam, A., et al: ‘Tracking endocardial motion via multiple model filtering’, IEEE Trans. Biomed. Eng., 2010, 57, (8), pp. 20012010.
    17. 17)
      • 17. Duan, Q., Angelini, E., Homma, S., et al: ‘Tracking endocardium using optical flow along iso-value curve’. Conf. Proc. IEEE Engineering in Medicine Biology Society, 2006, vol. 1, pp. 707710.
    18. 18)
      • 18. Esther Leung, K.Y., Danilouchkine, M.D., van Stralen, M., et al: ‘Tracking left ventricular borders in 3D echocardiographic sequences using motion-guided optical flow’. Proc. Medical Imaging: Image Processing, 2009, p. 7259.
    19. 19)
      • 19. Brieva, J., Moya-Albor, E., Escalante-Ramírez, B.: ‘A level set approach for left ventricle detection in CT images using shape segmentation and optical flow’. Proc. SPIE 9287, 10th International Symp. on Medical Information Processing and Analysis, 92870K, January 28, 2015, doi: 10.1117/12.2073869.
    20. 20)
      • 20. Duan, Q., Angelini, E., Gerard, O., et al: ‘Comparing optical-flow based methods for quantification of myocardial deformations on RT3D ultrasound’. IEEE International Symp. on Biomedical Imaging, 2006, pp. 173176.
    21. 21)
      • 21. Fahmy, A.S., Al-Agamy, A.O., Khalifa, A.: ‘Myocardial segmentation using contour-constrained optical flow tracking’. STACOM, 2012, pp. 120128.
    22. 22)
      • 22. Wang, Z.Z.: ‘An efficient and robust method for automatically identifying the left ventricular boundary in cine magnetic resonance images’, IEEE Trans. Autom. Sci. Eng., 2016, 13, (2), pp. 536542.
    23. 23)
      • 23. Lucas, B., Kanade, T.: ‘An iterative image registration technique with an application to stereo vision’. Proc. Seventh International Joint Conf. on Artificial Intelligence, Vancouver, 1981, pp. 674679.
    24. 24)
      • 24. Horn, B.K.P., Schunck, B.G.: ‘Determining optical flow’, Artif. Intell., 1981, 17, pp. 185203.
    25. 25)
      • 25. Alvarez, L., Esclarín, J., Lefébure, M., et al: ‘A PDE model for computing the optical flow’. Proc. XVI Congresso de Ecuaciones Diferenciales y Aplicaciones, Las Palmas, 1999, pp. 13491356.
    26. 26)
      • 26. Nambakhsh, C.M.S., Yuan, J., Punithakumar, K., et al: ‘Left ventricle segmentation in MRI via convex relaxed distribution matching’, Med. Image Anal., 2013, 17, (8), pp. 10101024.
    27. 27)
      • 27. Fantini, F., Barletta, G., Voegelin, M.R., et al: ‘Analysis of the shape of the left ventricle by studying the regional curvature and power spectrum. II. Morphologic changes in post-infarction ischemic heart disease’, G. Ital. Cardiol., 1989, 19, (8), pp. 664673.
    28. 28)
      • 28. Cignoni, P., Montani, C., Scopigno, R.: ‘DeWall: a fast divide and conquer delaunay triangulation algorithm in Ed’, Comput. Aided Design, 1998, 30, (5), pp. 333341.
    29. 29)
      • 29. Sezgin, M., Sankur, B.: ‘Survey over image thresholding techniques and quantitative performance evaluation’, J. Electron. Imaging, 2004, 13, (1), pp. 146165.
    30. 30)
      • 30. Otsu, N.: ‘A threshold selection method from gray level histogram’, IEEE Trans. Syst. Man Cybern. SMC-9, 1979, 9, (1), pp. 6266.
    31. 31)
      • 31. Wong, A.K.C., Sahoo, P.K.: ‘A gray-level thresholding selection method based on maximum entropy principle’, IEEE Trans. Syst. Man Cybern., 1989, 19, (4), pp. 866871.
    32. 32)
      • 32. Wang, Z.Z.: ‘Monitoring of GMAW weld pool from the reflected laser lines for real time control’, IEEE Trans. Ind. Inform., 2014, 10, (4), pp. 20732083.
    33. 33)
      • 33. Wang, Z.Z.: ‘A semi-automatic method for robust and efficient identification of neighboring muscle cells’, Pattern Recogn., 2016, 53, pp. 300312.
    34. 34)
      • 34. Wang, Z.Z.: ‘A new approach for segmentation and quantification of cells or nanoparticles’, IEEE Trans. Ind. Inform., 2016, 12, (3), pp. 962971.
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