Automatic Prostate Segmentation in MR Images with a Probabilistic Active Shape Model

Segmentation of the prostate gland in Magnetic Resonance (MR) images is an important task for image-guided prostate cancer therapy. The low contrast of the prostate to surrounding tissue in MR images makes automatic segmentation very challenging. In this paper, we propose an automatic approach for robust and accurate prostate segmentation in T2-weighted MR scans. We first employ a boosted prostate detector to locate the prostate in the images, and then use a Probabilistic Active Shape Model for the delineation of its contour. Our approach has been quantitatively evaluated on 50 MR images, on which we achieve a median dice coefficient of 0.85 (IQR: 0.09).