17. A non-transitory computer readable medium storing computer program instructions for automatic prostate segmentation in magnetic resonance (MR) image data, the computer program instructions when executed on a processor cause the processor to perform operations comprising: performing a first intensity normalization on the MR image to adjust for global contrast changes between the MR image and other MR scans; performing a second intensity normalization on the MR image to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image by: obtaining a mask image from the MR image using intensity thresholding, extracting a bright region from the MR image using the mask image, and calculating adjusted intensities to reduce an overall intensity within the bright region such that the adjusted intensities at a boundary of the bright region match a surrounding region in the MR image and gradient features within the bright region are retained; obtaining an initial prostate segmentation in the MR image by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL); and refining the initial prostate segmentation using one or more trained boundary classifiers.
17. A non-transitory computer readable medium storing computer program instructions for automatic prostate segmentation in magnetic resonance (MR) image data, the computer program instructions when executed on a processor cause the processor to perform operations comprising: performing a first intensity normalization on the MR image to adjust for global contrast changes between the MR image and other MR scans; performing a second intensity normalization on the MR image to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image by: obtaining a mask image from the MR image using intensity thresholding, extracting a bright region from the MR image using the mask image, and calculating adjusted intensities to reduce an overall intensity within the bright region such that the adjusted intensities at a boundary of the bright region match a surrounding region in the MR image and gradient features within the bright region are retained; obtaining an initial prostate segmentation in the MR image by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL); and refining the initial prostate segmentation using one or more trained boundary classifiers. 21. The non-transitory computer readable medium of claim 17 , wherein refining the initial prostate segmentation using one or more trained boundary classifiers comprises: refining a prostate surface mesh resulting from the initial prostate segmentation by calculating a displacement for each of a plurality of vertices of the mesh along a surface normal using the one or more trained boundary classifiers; and projecting a shape of the refined prostate surface mesh to a learned statistical shape space of the prostate.