Department of Computer Science and Information Engineering, National Chin Yi University of Technology, Taichung 41170, Taiwan.
Department of Interaction Design, Chang Jung Christian University, Tainan 71101, Taiwan.
J Healthc Eng. 2018 Jun 13;2018:7097498. doi: 10.1155/2018/7097498. eCollection 2018.
Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.
三维（3D）医学图像分割用于分割 3D 医学图像中的目标（病变或器官）。通过这个过程，可以获得 3D 目标信息；因此，这项技术是医学诊断的重要辅助工具。虽然一些方法已经被证明在二维（2D）图像分割中是成功的，但它们在 3D 情况下的直接应用并不令人满意。为了从 3D MR 图像中获得更精确的肿瘤分割结果，在本文中，我们提出了一种称为三维形状加权水平集方法（3D-SLSM）的方法。所提出的方法首先将在 2D 图像分割方面表现出色的 LSM 转换为适用于 3D 图像模型整体计算的 3D 算法，从而提高了计算的效率和准确性。然后，根据体积的变化，为每个 3D-SLSM 迭代过程添加一个 3D 形状加权值。除了提高收敛速度和消除背景噪声外，该形状加权值还使分割轮廓更接近实际肿瘤边界。为了对 3D-SLSM 进行定量分析并检验其在临床应用中的可行性，我们将实验分为计算机模拟序列图像和实际乳腺 MRI 病例。然后，我们同时比较了各种现有的 3D 分割方法。实验结果表明，3D-SLSM 对两种类型的实验图像都具有精确的分割结果。此外，3D-SLSM 在定量数据方面的表现优于现有的 3D 分割方法。
