School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.
Comput Biol Med. 2019 Nov;114:103432. doi: 10.1016/j.compbiomed.2019.103432. Epub 2019 Sep 4.
Parotid ducts (PDs) play an important role in the diagnosis and treatment of parotid lesions. Segmentation of PDs from Cone beam computed tomography (CBCT) images has a significant impact to the pathological analysis of the parotid gland. Although level set methods (LSMs) have achieved considerable success in medical imaging segmentation, it is still a challenging task for existing LSMs to precisely and self-adaptively segment PDs from parotid duct (PD) images with both noise, intensity inhomogeneity, and vague boundary. In this paper, we propose a novel Self-adaptive Weighted level set method via Local intensity Difference (SWLD) to comprehensively solve the above issues.
Firstly, a new adaptive weighted operator based on local intensity variance difference has been proposed to overcome the limitations of previous LSMs that are sensitive to parameters, which achieves the aim of automatic segmentation. Secondly, we introduce local intensity mean difference into the energy function to improve the curve evolution efficiency. Thirdly, we eliminate the effects of intensity inhomogeneity, noise, and boundary blur in the parotid image through a local similarity factor with two different neighborhood sizes.
Using the same dataset, segmentation of PDs is performed using the proposed SWLD algorithm and existing LSM algorithms. The mean Dice score for the proposed algorithm is 91.3%, and the corresponding mean Hausdorff distance (HD) is 1.746.
Experimental results demonstrate that the proposed algorithm is superior to many existing level set segmentation algorithms, and it can accurately and automatically segment the PDs even in complex gradient boundaries.
腮腺导管（PDs）在腮腺病变的诊断和治疗中起着重要作用。从锥形束计算机断层扫描（CBCT）图像中分割 PDs 对腮腺的病理分析有重大影响。尽管水平集方法（LSM）在医学成像分割方面已经取得了相当大的成功，但现有的 LSM 仍然难以精确地、自适应地分割腮腺导管（PD）图像中的 PD，这些图像存在噪声、强度不均匀和边界模糊等问题。在本文中，我们提出了一种新的基于局部强度差的自适应加权水平集方法（SWLD），以综合解决上述问题。
首先，提出了一种新的基于局部强度方差差的自适应加权算子，克服了以前的 LSM 对参数敏感的局限性，实现了自动分割的目的。其次，我们将局部强度均值差引入到能量函数中，以提高曲线演化效率。第三，我们通过具有两个不同邻域大小的局部相似性因子来消除腮腺图像中强度不均匀、噪声和边界模糊的影响。
使用相同的数据集，使用所提出的 SWLD 算法和现有的 LSM 算法对 PD 进行分割。所提出算法的平均 Dice 得分为 91.3%，对应的平均 Hausdorff 距离（HD）为 1.746。
实验结果表明，所提出的算法优于许多现有的水平集分割算法，即使在复杂的梯度边界中，它也能精确、自动地分割 PDs。
