Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI

Abstract

Accurate localization and contouring of prostate are crucial issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. We introduce a new method for automatic 3D segmentation of the prostate gland from magnetic resonance imaging (MRI) scans. A statistical shape model was used as an a priori knowledge, and gray levels distribution was modeled by fitting histogram modes with a Gaussian mixture. Markov fields were used to introduce contextual information regarding voxels' neighborhoods. Final labeling optimization is based on Bayesian a posteriori classification, estimated with the iterative conditional mode algorithm. We compared the accuracy of this method, free from any manual correction, with contours outlined by an expert radiologist. In 12 cases, including prostates with cancer and benign prostatic hypertrophy, the mean Hausdorff distance and overlap ratio were 9.94 mm and 0.83, respectively. This new automatic prostate MRI segmentation method produces satisfactory results, even at prostate's base and apex. The method is computationally feasible and efficient.