Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Graph cuts framework for kidney segmentation with prior shape constraints
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
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A common cause of kidney failure is autosomal dominant polycystic kidney disease (ADPKD). It is characterized by the growth of cysts in the kidneys and hence the growth of the entire kidneys with eventual failure in most cases by age 50. No preventive treatment for this condition is available. Preclinical drug treatment studies use an in vivo mouse model of the condition. The analysis of mice imaging data for such studies typically requires extensive manual interaction, which is subjective and not reproducible. In this work both untreated and treated mice have been imaged with a high field, 9.4T , MRI animal scanner and a reliable algorithm for the automated segmentation of the mouse kidneys has been developed. The algorithm first detects the region of interest (ROI) in the image surrounding the kidneys. A parameterized geometric shape for a kidney is registered to the ROI of each kidney. The registered shapes are incorporated as priors to the graph cuts algorithm used to extract the kidneys. The accuracy of the automated segmentation has been demonstrated by comparing it with a manual segmentation. The processing results are also consistent with the literature for previous techniques.