Integrated four dimensional registration and segmentation of dynamic renal MR images

Authors:
Ting Song;Vivian S. Lee;Henry Rusinek;Samson Wong;Andrew F. Laine
Affiliations:
Department of Biomedical Engineering, Columbia University, New York, NY;Department of Radiology, New York University Medical Center, New York, NY;Department of Radiology, New York University Medical Center, New York, NY;Department of Radiology, New York University Medical Center, New York, NY;Department of Biomedical Engineering, Columbia University, New York, NY
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

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 segmen-tation 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.