Song Ting, Lee Vivian S, Rusinek Henry, Wong Samson, Laine Andrew F
Department of Biomedical Engineering, Columbia University, New York, NY, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):758-65. doi: 10.1007/11866763_93.
In this paper a novel approach for the registration and segmentation of dynamic contrast enhanced renal MR images is presented. This integrated method is motivated by the observation of the reciprocity between registration and segmentation in 4D time-series images. Fully automated Fourier-based registration with sub-voxel accuracy and semi-automated time-series segmentation were intertwined to improve the accuracy in a multi-step fashion. We have tested our algorithm on several real patient data sets. Clinical validation showed remarkable and consistent agreement between the proposed method and manual segmentation by experts.
本文提出了一种用于动态对比增强肾脏磁共振图像配准和分割的新方法。这种集成方法的灵感来自于对四维时间序列图像中配准和分割之间相互关系的观察。具有亚体素精度的全自动傅里叶配准和半自动时间序列分割相互交织，以多步骤方式提高准确性。我们在几个真实患者数据集上测试了我们的算法。临床验证表明，该方法与专家手动分割之间存在显著且一致的一致性。
