A novel 3D segmentation approach for segmenting the prostate from dynamic contrast enhanced MRI using current appearance and learned shape prior

Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, we propose, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement Magnetic Resonance Images (DCE-MRI). The framework is based on a maximum aposteriori (MAP) estimate of a new log-likelihood function that consists of three descriptors: (i) 1^st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially invariant 2^nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate DCE-MRI data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate volume. The spatial interactions between the prostate voxels are modeled by a 3D 2^nd-order rotation-variant Markov-Gibbs random field (MGRF) of object/background labels with analytically estimated potentials. Experiments with real in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.