Journal of Optoelectronics · Laser, Volume. 35, Issue 6, 596(2024)

Left ventricle segmentation based on non-zero level set preserving convexity

LI Ji1,2,3、* and HU Jinping1
Author Affiliations
  • 1School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China
  • 2Chongqing Key Laboratory of Statistical Intelligent Computing and Monitoring, Chongqing Technology and Business University, Chongqing 400067
  • 3School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
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    References(11)

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    [2] [2] KHENED M, ALEX V, KRISHNAMURTHI G. Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest[C]//Statistical Atlases and Computational Models of the Heart, September 10-14, 2017, Quebec City, Canada. Cham: Springer, 2018: 140-151.

    [3] [3] BAUMGARTNER C F, KOCH L M, POLLEFEYS M, et al. An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation[C]//Statistical Atlases and Computational Models of the Heart, September 10-14, 2017, Quebec City, Canada. Cham: Springer, 2018: 111-119.

    [7] [7] LIU Y, CAPTUR G, MOON J C, et al. Distance regularized two level sets for segmentation of left and right ventricles from cine-MR[J]. Journal of Magnetic Resonance Imaging, 2016, 34(5): 699-706.

    [8] [8] YAN S, TAI XC, LIU J, et al. Convexity shape prior for level set based image segmentation method.[J]. IEEE Transactions on Image Processing, 2020, 29: 7141-7152.

    [9] [9] LI C, XU C, GUI C, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12): 3243-3254.

    [10] [10] SHI X, TANG L J, YANG X P, et al. A fast convexity preserving level set method for segmentation of cardiac left ventricl[C]//2nd International Symposium on Image Computing and Digital Medicine, October 13-15, 2018, Chengdu, China. New York: Association for Computing Machinery, 2018: 51-54.

    [11] [11] XIE L P, QI J, PAN L L, et al. Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images[J]. Neurocomputing, 2020, 376: 166-179.

    [14] [14] SHI X, LI C M. Convexity preserving level set for left ventricle segmentation[J]. Journal of Magnetic Resonance Imaging, 2021, 78: 109-118.

    [15] [15] MSP M, ROH, SERMESANT M, PENNEC X. Automatic multi-atlas segmentation of myocardium with SVF-NET[C]//Statistical Atlases and Computational Models of the Heart, September 10-14, 2017, Quebec City, Canada. ChamSpringer, 2018: 170-177.

    [16] [16] YANG X, BIAN C, YU L, et al. Class-balanced deep neural network for automatic ventricular structure segmentation[C]//Statistical Atlases and Computational Models of the Heart, September 10-14, 2017, Quebec City, Canada. Cham: Springer, 2018: 152-160.

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    LI Ji, HU Jinping. Left ventricle segmentation based on non-zero level set preserving convexity[J]. Journal of Optoelectronics · Laser, 2024, 35(6): 596

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    Paper Information

    Category:

    Received: Dec. 14, 2022

    Accepted: Dec. 13, 2024

    Published Online: Dec. 13, 2024

    The Author Email: LI Ji (957947864@qq.com)

    DOI:10.16136/j.joel.2024.06.0844

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