Fraunhofer IGD, 64283 Darmstadt, Germany.
IEEE Trans Med Imaging. 2013 May;32(5):888-900. doi: 10.1109/TMI.2013.2242901. Epub 2013 Jan 25.
The 3D-segmentation of lymph nodes in computed tomography images is required for staging and disease progression monitoring. Major challenges are shape and size variance, as well as low contrast, image noise, and pathologies. In this paper, radial ray based segmentation is applied to lymph nodes. From a seed point, rays are cast into all directions and an optimization technique determines a radius for each ray based on image appearance and shape knowledge. Lymph node specific appearance cost functions are introduced and their optimal parameters are determined. For the first time, the resulting segmentation accuracy of different appearance cost functions and optimization strategies is compared. Further contributions are extensions to reduce the dependency on the seed point, to support a larger variety of shapes, and to enable interaction. The best results are obtained using graph-cut on a combination of the direction weighted image gradient and accumulated intensities outside a predefined intensity range. Evaluation on 100 lymph nodes shows that with an average symmetric surface distance of 0.41 mm the segmentation accuracy is close to manual segmentation and outperforms existing radial ray and model based methods. The method's inter-observer-variability of 5.9% for volume assessment is lower than the 15.9% obtained using manual segmentation.
在计算机断层扫描图像中，对淋巴结进行三维分割是进行分期和疾病进展监测所必需的。主要的挑战是形状和大小的变化，以及对比度低、图像噪声和病变等问题。本文将基于放射射线的分割方法应用于淋巴结。从一个种子点开始，射线向各个方向发射，然后根据图像外观和形状知识，使用优化技术为每条射线确定半径。引入了特定于淋巴结的外观成本函数，并确定了它们的最佳参数。本文首次比较了不同外观成本函数和优化策略的分割准确性。此外，还提出了一些扩展方法来减少对种子点的依赖，支持更多种类的形状，并实现交互。在对方向加权图像梯度和预定义强度范围外的累积强度的组合上使用图割进行优化，可获得最佳结果。在 100 个淋巴结上的评估表明，平均对称表面距离为 0.41 毫米，分割准确性接近手动分割，优于现有的基于放射射线和模型的方法。该方法的体积评估观察者间变异性为 5.9%，低于手动分割的 15.9%。
