School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang 310027, PR China.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
Phys Med. 2019 Jan;57:123-136. doi: 10.1016/j.ejmp.2018.12.032. Epub 2019 Jan 5.
The segmentation of organs and lesions from medical images is a challenging task due to the presents of noise, intensity inhomogeneity, blurry/weak boundaries. In this paper, a point distance shape constraint is proposed and incorporated in the level set framework for the segmentation of objects with various shapes.
The proposed shape constraint is a linear combination of the Euclidean distance of user selected points. By selecting different numbers of points, it can generate different shape constraints and therefore is more flexible in dealing with different shapes. Then this shape constraint is incorporated into the variational level set framework. A convex relaxation is applied to get a convex model which can be efficiently solved by a primal-dual hybrid gradient algorithm.
The proposed algorithm is tested on 60 CT images with the nodular type of hepatic cellular cancer (HCC), 100 ultrasound kidney images, 20 prostate MR images, 20 lumbar CT images and 100 transrectal ultrasound prostate images. The algorithms performance is evaluated using a number of metrics by comparison with expert delineations. The validation results show that, for five datasets mentioned previously, the average DSCs of the proposed algorithm are 95.6% ± 1.4%, 94.3% ± 3.1%, 91.3% ± 3.8%, 92.7% ± 1.5% and 94.4% ± 2.2% respectively. Both quantitative and qualitative evaluation confirm that the proposed method can provide more accurate segmentation than four state-of-the-art methods.
The proposed point distance shape constraint segmentation model can accurately segment organs and lesions with a number of shapes in medical images.
由于存在噪声、强度不均匀、边界模糊/弱等问题，医学图像中的器官和病变分割是一项具有挑战性的任务。在本文中，提出了一种点距离形状约束，并将其纳入水平集框架中，用于分割具有各种形状的目标。
所提出的形状约束是用户选择的点的欧几里得距离的线性组合。通过选择不同数量的点，可以生成不同的形状约束，因此在处理不同形状时更加灵活。然后将这种形状约束纳入变分水平集框架中。应用凸松弛得到一个凸模型，该模型可以通过原对偶混合梯度算法有效地求解。
该算法在 60 个肝细胞核性肝癌（HCC）结节型 CT 图像、100 个超声肾图像、20 个前列腺 MR 图像、20 个腰椎 CT 图像和 100 个经直肠超声前列腺图像上进行了测试。通过与专家勾画的比较，使用多种指标来评估算法的性能。验证结果表明，对于前面提到的五个数据集，所提出算法的平均 DSC 分别为 95.6%±1.4%、94.3%±3.1%、91.3%±3.8%、92.7%±1.5%和 94.4%±2.2%。定量和定性评估都证实，所提出的方法可以比四种最先进的方法提供更准确的分割。
所提出的点距离形状约束分割模型可以准确地分割医学图像中具有多种形状的器官和病变。
