Department of Computer Science, Swiss Federal Institute of Technology (ETH) Zurich, Room CAB F 61.1, Universitätstrasse 6, 8092, Zurich, Switzerland.
J Digit Imaging. 2013 Apr;26(2):173-82. doi: 10.1007/s10278-012-9497-z.
We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel's dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separately.
我们提出了一种利用时间信息的动态心脏灌注图像的联合分割和分组配准方法。灌注图像的性质使得分组配准特别有吸引力,因为可以使用整个图像序列的时间信息。配准的目的是使强度信号的平滑度最大化,而分割则使具有相同分割标签的像素与其他像素的相似度最小化。成本函数使用 B 样条以迭代的方式进行优化。对真实患者数据集的测试表明,与其他两种方法相比,我们的方法显示出更低的配准误差和更高的分割准确性。这归因于在一个框架中同时使用时间信息进行分组配准以及相互补充的配准和分割信息,而其他方法则分别解决这两个问题。