University of Electronic Science and Technology of China, Chengdu 611731, China.
University of Electronic Science and Technology of China, Chengdu 611731, China.
Magn Reson Imaging. 2021 May;78:109-118. doi: 10.1016/j.mri.2021.02.003. Epub 2021 Feb 14.
In clinical applications of cardiac left ventricle (LV) segmentation, the segmented LV is desired to include the cavity, trabeculae, and papillary muscles, which form a convex shape. However, the intensities of trabeculae and papillary muscles are similar to myocardium. Consequently, segmentation algorithms may easily misclassify trabeculae and papillary muscles as myocardium. In this paper, we propose a level set method with a convexity preserving mechanism to ensure the convexity of the segmented LV. In the proposed level set method, the curvature of the level set contours is used to control their convexity, such that the level set contour is finally deformed as a convex shape. The experimental results and the comparison with other level set methods show the advantage of our method in terms of segmentation accuracy. Compared with the state-of-the-art methods using deep-learning, our method is able to achieve comparable segmentation accuracy without the need for training, while the deep-learning based method requires a large set of training data and high-quality manual segmentation. Therefore, our method can be conveniently used in situation where training data and their manual segmentation are not available.
在心脏左心室 (LV) 分割的临床应用中，期望分割的 LV 包括腔、小梁和乳头肌，这些组织形成一个凸形。然而，小梁和乳头肌的强度与心肌相似。因此，分割算法可能很容易将小梁和乳头肌错误地分类为心肌。在本文中，我们提出了一种具有凸保持机制的水平集方法，以确保分割的 LV 的凸性。在提出的水平集方法中，使用水平集轮廓的曲率来控制它们的凸性，使得水平集轮廓最终变形为凸形。实验结果和与其他水平集方法的比较表明了我们的方法在分割准确性方面的优势。与使用深度学习的最新方法相比，我们的方法能够实现可比的分割准确性，而无需训练，而基于深度学习的方法需要大量的训练数据和高质量的手动分割。因此，我们的方法可以方便地用于没有训练数据和其手动分割的情况下。
