Application of a new segmentation tool based on interactive simplex meshes to cardiac images and pulmonary MRI data
Application of a new segmentation tool based on interactive simplex meshes to cardiac images and pulmonary MRI data
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DOI：
10.1016/j.acra.2006.12.001
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发表时间：
2007-03-01
影响因子：
4.8
通讯作者：
Wolf, Ivo
中科院分区：
文献类型：
--
作者：
Boettger, Thomas;Kunert, Tobias;Wolf, Ivo
基本原理和目标。医学图像分割仍然非常耗时，因此很少集成到临床常规中。各种三维 (3D) 分割方法可以促进这项工作，但由于此类模型的初始化和参数化复杂，因此很少在临床设置中使用。材料和方法。我们开发了一种基于可变形单纯形网格的新型半自动 3D 分割工具。用户可以在原始图像数据中定义吸引点。新的变形算法保证表面模型将通过这些交互式设定点。用户可以直接影响可变形模型的演化，并在分割过程中获得直接反馈结果。该分割工具针对心脏图像数据和磁共振成像肺部图像进行了评估。与手动分割的比较显示出较高的准确性。在某些情况下，可以减少描绘各种结构所需的时间。该模型对输入数据和模型初始化中的噪声不敏感。结论。该工具适用于任何类型的 3D 或 3D 时间分辨医学图像数据的快速交互式分割。它使临床医生能够影响复杂的 3D 分割算法并使该算法可控。数据质量越好，需要的交互就越少。当处理后的图像质量较低时，该工具仍然可以工作。
Rationale and Objectives. Medical image segmentation is still very time consuming and is therefore seldom integrated into clinical routine. Various three-dimensional (3D) segmentation approaches could facilitate the work, but they are rarely used in clinical setups because of complex initialization and parametrization of such models.Materials and Methods. We developed a new semiautomatic 3D-segmentation tool based on deformable simplex meshes. The user can define attracting points in the original image data. The new deformation algorithm guarantees that the surface model will pass through these interactively set points. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process.Results. The segmentation tool was evaluated for cardiac image data and magnetic resonance imaging lung images. Comparison with manual segmentation showed high accuracy. Time needed for delineation of the various structures could be reduced in some cases. The model was not sensitive to noise in the input data and model initialization.Conclusions. The tool is suitable for fast interactive segmentation of any kind of 3D or 3D time-resolved medical image data. It enables the clinician to influence a complex 3D-segmentation algorithm and makes this algorithm controllable. The better the quality of the data, the less interaction is required. The tool still works when the processed images have low quality.