Multiple Mean Models of Statistical Shape and Probability Priors for Automatic Prostate Segmentation.
Soumya Ghose, Arnau Oliver, Robert Martí, Xavier Lladó, Jordi Freixenet, Jhimli Mitra, Joan C. Vilanova, Josep Comet, Fabrice Mériaudeau · 2011
Abstract. Low contrast of the prostate gland, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow re-gions, speckle and significant variations in prostate shape, size and in-ter dataset contrast in Trans Rectal Ultrasound (TRUS) images chal-lenge computer aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose a probabilistic framework for auto-matic initialization and propagation of multiple mean parametric models derived from principal component analysis of shape and posterior prob-ability information of the prostate region to segment the prostate. Un-like traditional statistical models of shape and intensity priors we use posterior probability of the prostate region to build our texture model of the prostate and use the information in initialization and propaga-tion of the mean model. Furthermore, multiple mean models are used compared to a single mean model to improve segmentation accuracies. The proposed method achieves mean Dice Similarity Coefficient (DSC)