JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (7): 32-47.doi: 10.6040/j.issn.1671-9352.0.2024.063

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Left ventricular MRI segmentation based on nonzero level sets convexity preserving algorithm

LI Ji1,2,3, LIU Aiwen1,2*, QIN Liu1,2   

  1. 1. School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China;
    2. Chongqing Key Laboratory of Statistical Intelligent Computing and Monitoring, Chongqing Technology and Business University, Chongqing, 400067, China;
    3. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
  • Published:2025-07-01

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Abstract: Accurate segmentation of the left ventricle in clinical applications requires maintaining a convex shape that encompasses the left ventricle cavity, trabeculae, and papillary muscles. The nonzero level set convexity preserving model, a novel cardiac magnetic resonance imaging segmentation model incorporating an enhanced distance regularization term and a nonzero level set convexity preserving term is introduced. By leveraging the curvature of the level set contour, the model effectively promotes convexity, ensuring the contour evolves into a convex shape. Evaluated on the ACDC MICCAI 2017 datasets, the model achieved a mean Dice coefficient of 0.961 and 0.936 in end-diastole and end-systole phases, respectively, alongside a mean Hausdorff distance of 4.89 and 5.79. Notably, the model eliminates the need for manual annotation of training data and time-consuming learning processes, while achieving segmentation accuracy and robustness comparable to deep learning-based left ventricle segmentation models.

Key words: nonzero level set, convexity preserving, level set method, left ventricle segmentation, distance regularization, double well potential function

CLC Number: 

  • TP391
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