Segmenting the prostate and rectum in CT imagery using anatomical constraints

The automatic segmentation of the prostate and rectum from 3D computed tomography (CT) images is still a challenging problem, and is critical for image-guided therapy applications. We present a new, automatic segmentation algorithm based on deformable organ models built from previously segmented training data. The major contributions of this work are a new segmentation cost function based on a Bayesian framework that incorporates anatomical constraints from surrounding bones and a new appearance model that learns a nonparametric distribution of the intensity histograms inside and outside organ contours. We report segmentation results on 185 datasets of the prostate site, demonstrating improved performance over previous models. (C) 2010 Elsevier B.V. All rights reserved.