Research Progress of Automatic Right Ventricle Segmentation Based on Cardiac Cine Magnetic Resonance Image_Journal of Biomedical Engineering

Authors：

LIYa , WANGLijia ,  NIEShengdong

School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;

Corresponding author：

NIEShengdong, Email: nsd4647@163.com

Keywords：

cardiac cine magnetic resonance image; right ventricle segmentation; evaluation methods; study development

DOI：

10.7507/1001-5515.20160190

Video：

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Heart diseases seriously threaten people's health. More and more functional evaluation of cardiac right ventricle has been considered in the clinical diagnosis in addition to the classical functional evaluation of cardiac left ventricle. It is very important to evaluate the functional parameters of right ventricle in clinical heart disease diagnosis, especially when the ejection fraction of left ventricle is very low. Right ventricular segmentation is needed for the functional evaluation. However, right ventricular segmentation has been difficult due to its thin myocardium, complex structure and significant individual variability. Cine cardiac magnetic resonance image is a golden standard in clinical functional evaluation of cardiac ventricle. In the present paper, we summarize the classic segmentation approaches, evaluation methods and their development, which can help the researchers in the related field have a quick and basic understanding to the right ventricle segmentation.

Citation： LIYa, WANGLijia, NIEShengdong. Research Progress of Automatic Right Ventricle Segmentation Based on Cardiac Cine Magnetic Resonance Image. Journal of Biomedical Engineering, 2016, 33(6): 1203-1208. doi: 10.7507/1001-5515.20160190 Copy

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1. BAUR L. Magnetic resonance imaging: the preferred imaging method for evaluation of the right ventricle[J]. International Journal of Cardiovascular Imaging, 2008, 24(7): 699-700.
2. CAUDRON J, FARES J, VIVIER P, et al. Diagnostic accuracy and variability of three semi-quantitative methods for assessing right ventricular systolic function from cardiac MRI in patients with acquired heart disease[J]. EurRadiol, 2011, 21(10): 2111-2120.
3. MAHAPATRA D. An automated approach to cardiac RV segmentation from MRI using learned semantic information and graph Cuts[J]. J Digit Imaging, 2014, 27(6): 1-18.
4. GROSGEORGE D, PETITJEAN C, CAUDRON J A, et al. Automatic cardiac ventricle segmentation in MR images: a validation study[J]. Int J Comput Assist RadiolSurg, 2011, 6(5): 573-581.
5. WANG Lijia, PEI Mengchao, CODELLA N C, et al. Left ventricle: fully automated segmentation based on spatiotemporal continuity and myocardium information in cine cardiac magnetic resonance imaging (LV-FAST)[J]. Biomed Res Int, 2015, 2015: 367583.
6. 刘鸿翔, 陈宇珂.基于图割和K聚类的CT图像分割[J].医疗卫生装备,2016,37(1):21-24.
7. 辛月兰. 基于图割的图像分割综述[J].微型电脑应用,2012,28(9):1-5, 8.
8. MAIER O M O, JIMÉNEZ D, SANTOS A, et al. Segmentation of RV in 4D cardiac MR volumes using region-merging graph cuts[C]//2012 Computing in Cardiology. Krakow, Poland: IEEE, 2012: 697-700.
9. COCOSCO C A, NIESSEN W J, NETSCH T, et al. Automatic image-driven segmentation of the ventricles in cardiac cine MRI[J]. Journal of Magnetic Resonance Imaging, 2008, 28(2): 366-374.
10. AMIN Y, BANDAY S A, MIR A H. A comparative study on left and right endocardium segmentation using gradient vector field and adaptive diffusion flow algorithms[J]. International Journal of Bio-Science and Bio-Technology, 2016, 8(1): 105-120.
11. ABI-NAHED J, JOLLY M P, YANG G Z. Robust active shape models: A robust, generic and simple automatic segmentation tool[C]//LARSEN R, NIELSEN M, SPORRING J. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2006. Berlin Heidelberg: Springer Berlin Heidelberg, 2006:1-8.
12. CHUI H, RANGARAJAN A. A new algorithm for non-rigid point matching[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000. Hilton Head Island, US: IEEE, 2000, 2: 44-51.
13. MITCHELL S C, LELIEVELDT B P, VAN DER GEEST R J, et al. Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MRimages[J]. IEEE Trans Med Imaging, 2001, 20(5): 415-423.
14. COOTES T F, TAYLOR C J, LANITIS A. Active shape models: Evaluation of a multi-resolution method for improving image search[C]//Proceedings of British Machine Vision Conference. York, UK, 1994: 327-338.
15. 曹一挥. 基于多图谱医学图像分割的技术研究[D].北京:中国科学院大学,2013.
16. HAN X, HOOGEMAN M S, LEVENDAG P C, et al. Atlas-based auto-segmentation of head and neck CT images[C]//METAXAS D, AXEL L, FICHTINGER G. International Conference on Medical Image Computing & Computer-assisted Intervention. Berlin, Heidelberg: Springer-Verlag, 2008: 434-441.
17. HUO J, WANG G, WU Q M J, et al. Label fusion for multi-atlas segmentation based on majority voting[C]//KAMEL M S, CAMPILHO A C, CAMPILHO A C. International Conference Image Analysis and Recognition. Niagara Falls, Canada: Springer International Publishing, 2015: 100-106.
18. OU Y, DOSHI J, ERUS G, et al. Multi-atlas segmentation of the cardiac MR right ventricle[C/OL].(2016-04-1)[2016-08-30]http://www.nmr.mgh.harvard.edu/~you2/publications/MICCAI12_CARDIAC_RV_SBIA_v3.pdf.
19. 张耀楠, 陈传慎,康雁.基于仿射传播聚类选择的多Atlas右心室精准分割[J].东北大学学报:自然科学版,2014,35(6):795-799.
20. PETITJEAN C, ZULUAGA M A, BAI Wenjia, et al. Right ventricle segmentation from cardiac MRI: a collation study[J]. Med Image Anal, 2015, 19(1): 187-202.
21. ZHENG S, JAYASUMANA S, ROMERA-PAREDES B, et al. Conditional random fields as recurrent neural networks[C]//IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015:1529-1537.
22. KAMNITSAS K, LEDIG C, NEWCOMBE V F J, et al. Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation[EB/OL]. (2016-04-1)[2016-08-30] http://arxiv.org/pdf/1603.05959.pdf.
23. TRAN P V. A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI[EB/OL]. (2016-04-2)[2016-08-30]http://arxiv.org/pdf/1604.00494.pdf.
24. 曹贵宝. 随机森林和卷积神经网络在神经细胞图像分割中的应用研究[D].济南:山东大学,2014.
25. CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL].(2016-06-7)[2016-08-30] http://arxiv.org/pdf/1412.7062.pdf.
26. HUTTENLOCHER D P, KLANDERMAN G A, RUCKLIDGE W J. Comparing images using the Hausdorffdistance[J]. IEEE Trans Pattern Anal Mach Intell, 1993, 15(9): 850-863.

Journal of Biomedical Engineering

Research Progress of Automatic Right Ventricle Segmentation Based on Cardiac Cine Magnetic Resonance Image

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