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Left ventricular MRI segmentation based on nonzero level sets convexity preserving algorithm
- LI Ji, LIU Aiwen, QIN Liu
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JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2025, 60(7):
32-47.
doi:10.6040/j.issn.1671-9352.0.2024.063
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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.
