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International Journal of Computer Assisted Radiology and Surgery

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01.11.2016 | Original Article

An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images

verfasst von: Yurun Ma, Li Wang, Yide Ma, Min Dong, Shiqiang Du, Xiaoguang Sun

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2016

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Abstract

Purpose

Accurate segmentation of left ventricle (LV) is essential for the cardiac function analysis. However, it is labor intensive and time consuming for radiologists to delineate LV boundary manually. In this paper, we present a novel self-correcting framework for the fully automatic LV segmentation.

Methods

Firstly, a time-domain method is designed to extract a rectangular region of interest around the heart. Then, the simplified pulse-coupled neural network (SPCNN) is employed to locate the LV cavity. Different from the existing approaches, SPCNN can realize the self-correcting segmentation due to its parameter controllability. Subsequently, the post-processing based on the maximum gradient searching is proposed to obtain the accurate endocardium. Finally, a new external force based on the shape similarity is defined and integrated into the gradient vector flow (GVF) snake with the balloon force to segment the epicardium.

Results

We obtain encouraging segmentation results tested on the database provided by MICCAI 2009. The average percentage of good contours is 92.26 %, the average perpendicular distance is 2.38 mm, and the overlapping dice metric is 0.89. Besides, the experiment results show good correlations between the automatic segmentation and the manual delineation (for the LV ejection fraction and the LV myocardial mass, the correlation coefficients R are 0.9683 and 0.9278, respectively).

Conclusion

We propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.

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Petitjean C, Dacher JN (2011) A review of segmentation methods in short axis cardiac MR images. Med Image Anal 15(2):169–184. doi:10.1016/j.media.2010.12.004 CrossRefPubMed

Kurkure U, Pednekar A, Muthupillai R, Flamm SD, Kakadiaris IA (2009) Localization and segmentation of left ventricle in cardiac cine-MR images. IEEE Trans Biomed Eng 56(5):1360–1370. doi:10.1109/TBME.2008.2005957 CrossRefPubMed

Hu H, Liu H, Gao Z, Huang L (2013) Hybrid segmentation of left ventricle in cardiac MRI using gaussian-mixture model and region restricted dynamic programming. Magn Reson Imaging 31(4):575–584. doi:10.1016/j.mri.2012.10.004 CrossRefPubMed

Huang S, Liu J, Lee LC, Venkatesh SK, Teo LLS, Au C, Nowinski WL (2011) An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images. J Digit Imaging 24(4):598–608. doi:10.1007/s10278-010-9315-4 CrossRefPubMed

Liu H, Hu H, Xu X, Song E (2012) Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming. Acad Radiol 19(6):723–731. doi:10.1016/j.acra.2012.02.011 CrossRefPubMed

Tsai I-C, Huang Y-L, Liu P-T, Chen M-C (2012) Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography. Int J Comput Assist Radiol Surg 7(5):737–751. doi:10.1007/s11548-012-0688-3 CrossRefPubMed

Albà X, Figueras i Ventura RM, Lekadir K, Tobon-Gomez C, Hoogendoorn C, Frangi AF (2014) Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints. Magn Reson Med 72(6):1775–1784. doi:10.1002/mrm.25079

Tufvesson J, Hedström E, Steding-ehrenborg K, Carlsson M, Arheden H, Heiberg E (2015) Validation and development of a new automatic algorithm for time-resolved segmentation of the left ventricle in magnetic resonance imaging. J Cardiovasc Magn Reson 17(Suppl 1):68–80. doi:10.1186/1532-429X-17-S1-P68 CrossRef

Dakua SP (2015) LV segmentation using stochastic resonance and evolutionary cellular automata. Int J Pattern Recognit Artif Intell 29:1–26. doi:10.1142/S0218001415570025 CrossRef

10.

Dakua SP (2014) AnnularCut: a graph-cut design for left ventricle segmentation from magnetic resonance images. IET Image Process 8(1):1–11. doi:10.1049/iet-ipr.2013.0088 CrossRef

11.

Wang Y, Jia Y (2006) Segmentation of the left ventricle from MR images via snake models incorporating shape similarities. In: Proceeding international conference on image processing ICIP, pp 213–216. doi:10.1109/ICIP.2006.312458

12.

Grosgeorge D, Petitjean C, Caudron J, Fares J, Dacher JN (2011) Automatic cardiac ventricle segmentation in MR images: a validation study. Int J Comput Assist Radiol Surg 6(5):573–581. doi:10.1007/s11548-010-0532-6 CrossRefPubMed

13.

Constantinides C, Roullot E, Lefort M, Frouin F (2012) Fully automated segmentation of the left ventricle applied to cine MR images: description and results on a database of 45 subjects. In: Proceeding annual international conference on IEEE Engineering in Medicine and Biology Society EMBS, pp 3207–3210. doi:10.1109/EMBC.2012.6346647

14.

Wu Y, Wang Y, Jia Y (2013) Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model. Comput Vis Image Underst 117(9):990–1003. doi:10.1016/j.cviu.2012.12.008 CrossRef

15.

Queirós S, Barbosa D, Heyde B, Morais P, Vilaça JL, Friboulet D, Bernard O, D’hooge J (2014) Fast automatic myocardial segmentation in 4D cine CMR datasets. Med Image Anal 18(7):1115–1131. doi:10.1016/j.media.2014.06.001 CrossRefPubMed

16.

Faghih Roohi S, Aghaeizadeh Zoroofi R (2013) 4D statistical shape modeling of the left ventricle in cardiac MR images. Int J Comput Assist Radiol Surg 8(3):335–351. doi:10.1007/s11548-012-0787-1 CrossRefPubMed

17.

Qin X, Tian Y, Yan P (2015) Feature competition and partial sparse shape modeling for cardiac image sequences segmentation. Neurocomputing 149:904–913. doi:10.1016/j.neucom.2014.07.044 CrossRef

18.

Stebbing RV, Namburete AIL, Upton R, Leeson P, Noble JA (2015) Data-driven shape parameterization for segmentation of the right ventricle from 3D + t echocardiography. Med Image Anal 21(1):29–39. doi:10.1016/j.media.2014.12.002 CrossRefPubMed

19.

Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE). IEEE Trans Med Imaging 23(7):903–921. doi:10.1109/TMI.2004.828354 CrossRefPubMedPubMedCentral

20.

Lebenberg J, Lalande A, Clarysse P, Buvat I, Casta C, Cochet A, Constantinidès C, Cousty J, Cesare A, Jehan-Besson S, Lefort M, Najman L, Roullot E, Sarry L, Tilmant C, Frouin F, Garreau M (2015) Improved estimation of cardiac function parameters using a combination of independent automated segmentation results in cardiovascular magnetic resonance imaging. PLoS One 10(8):e0135715-1–e0135715-16. doi:10.1371/journal.pone.0135715 CrossRef

21.

Suinesiaputra A, Cowan BR, Finn JP, Fonseca CG, Kadish AH, Lee DC, Medrano-Gracia P, Warfield SK, Tao W, Young A (2012) Left ventricular segmentation challenge from cardiac MRI: a collation study. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 7085 LNCS:88–97. doi:10.1007/978-3-642-28326-0_9

22.

Radau P, Lu Y, Connelly K, Paul G, Dick A.J. WGA (2009) Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS Journal-Card MR Left Vent Segmentation Chall 2009. http://hdl.handle.net/10380/3070

23.

Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. doi:10.1109/TSMC.1979.4310076 CrossRef

24.

Lu Y, Radau P, Connelly K, Dick A, Wright G (2009) Segmentation of left ventricle in cardiac cine mri: An automatic image-driven method. In: Functional imaging and modeling of the heart. Springer, Berlin, Heidelberg, pp 339–347. doi:10.1007/978-3-642-01932-6_37

25.

Chen Y, Park SK, Ma Y, Ala R (2011) A new automatic parameter setting method of a simplified PCNN for image segmentation. IEEE Trans Neural Netw 22(6):880–892. doi:10.1109/TNN.2011.2128880 CrossRefPubMed

26.

Chou N, Wu J, Bai Bingren J, Qiu A, Chuang K-H (2011) Robust automatic rodent brain extraction using 3D pulse-coupled neural networks (PCNN). IEEE Trans Image Process 20(9):2554–2564. doi:10.1109/TIP.2011.2126587 CrossRefPubMed

27.

Gao C, Zhou D, Guo Y (2014) An iterative thresholding segmentation model using a modified pulse coupled neural network. Neural Process Lett 39(1):81–95. doi:10.1007/s11063-013-9291-z CrossRef

28.

Wei S, Hong Q, Hou M (2011) Automatic image segmentation based on PCNN with adaptive threshold time constant. Neurocomputing 74(9):1485–1491. doi:10.1016/j.neucom.2011.01.005 CrossRef

29.

Ma YD, Liu L, Zhan K, Wu YQ (2010) Pulse-coupled neural networks and one-class support vector machines for geometry invariant texture retrieval. Image Vis Comput 28(11):1524–1529. doi:10.1016/j.imavis.2010.03.006 CrossRef

30.

Stewart RD, Fermin I, Opper M (2002) Region growing with pulse-coupled neural networks: an alternative to seeded region growing. IEEE Trans Neural Netw 13(6):1557–1562. doi:10.1109/TNN.2002.804229 CrossRefPubMed

31.

Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369. doi:10.1109/83.661186

32.

Cohen LD (1991) On active contour models and balloons. CVGIP Image Underst 53(2):211–218. doi:10.1016/1049-9660(91)90028-N CrossRef

33.

Lalande A, Salve N, Comte A, Jaulent MC, Legrand L, Walker P, Cottin Y, Wolf J, Brunotte F (2004) Left ventricular ejection fraction calculation from automatically selected and processed diastolic and systolic frames in short-axis cine-MRI. J Cardiovasc Magn Reson 6(4):817–827. doi:10.1081/JCMR-200036143 CrossRefPubMed

34.

Constantinides C, Chenoune Y, Kachenoura N, Roullot E, Mousseaux E, Herment A, Frouin F (2009) Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models. MIDAS J-Card MR Left Vent Segmentation Chall. http://hdl.handle.net/10380/3108

35.

Cousty J, Najman L, Couprie M, Clément-Guinaudeau S, Goissen T, Garot J (2010) Segmentation of 4D cardiac MRI: automated method based on spatio-temporal watershed cuts. Image Vis Comput 28(8):1229–1243. doi:10.1016/j.imavis.2010.01.001 CrossRef

36.

Schaerer J, Casta C, Pousin J, Clarysse P (2010) A dynamic elastic model for segmentation and tracking of the heart in MR image sequences. Med Image Anal 14(6):738–749. doi:10.1016/j.media.2010.05.009 CrossRefPubMed

37.

Fonseca CG, Backhaus M, Bluemke DA, Britten RD, Do Chung J, Cowan BR, Dinov ID, Finn JP, Hunter PJ, Kadish AH, Lee DC, Lima JC, Medrano-Gracia P, Shivkumar K, Suinesiaputra A, Tao W, Young A (2011) The cardiac atlas project-an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16):2288–2295. doi:10.1093/bioinformatics/btr360 CrossRefPubMedPubMedCentral

38.

Suinesiaputra A, Medrano-Gracia P, Cowan BR, Young A (2015) Big heart data: advancing health informatics through data sharing in cardiovascular imaging. IEEE J Biomed Health Inform 19(4):1283–1290. doi:10.1109/JBHI.2014.2370952 CrossRefPubMed

39.

Lebenberg J, Buvat I, Lalande A, Clarysse P, Casta C, Cochet A, Constantinidès C, Cousty J, Cesare A, Jehan-Besson S, Lefort M, Najman L, Roullot E, Sarry L, Tilmant C, Garreau MG, Frouin F (2012) Nonsupervised ranking of different segmentation approaches: Application to the estimation of the left ventricular ejection fraction from cardiac cine MRI sequences. IEEE Trans Med Imaging 31(8):1651–1660. doi:10.1109/TMI.2012.2201737 CrossRefPubMed

Titel: An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images
verfasst von: Yurun Ma
Li Wang
Yide Ma
Min Dong
Shiqiang Du
Xiaoguang Sun
Publikationsdatum: 01.11.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 11/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-016-1429-9

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Abstract

Purpose

Methods

Results

Conclusion

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