CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
A Generic Probabilistic Active Shape Model for Organ Segmentation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Supervised Nonparametric Image Parcellation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Efficient selection of the most similar image in a database for critical structures segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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
Vessels-Cut: a graph based approach to patient-specific carotid arteries modeling
3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Anatomical structures segmentation by spherical 3d ray casting and gradient domain editing
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
A fully automated framework for renal cortex segmentation
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Kidney segmentation using graph cuts and pixel connectivity
Pattern Recognition Letters
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We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79mm. These results indicate that our method is accurate and robust for kidney segmentation.