Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation [chapter]

M. Freiman, A. Kronman, S. J. Esses, L. Joskowicz, J. Sosna
2010 Lecture Notes in Computer Science  
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
more » ... s 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.
doi:10.1007/978-3-642-15711-0_10 fatcat:ihfll6y3jzdabk6fo3xwcznsne