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In this paper we propose a novel segmentation method that integrates prior shape knowledge obtained from a 3D statistical model into the Markov Random Field (MRF) segmentation framework to deal with severe artifacts, noise and shape deformations. The statistical model is learned using a Probabilistic Principal Component Analysis (PPCA), which allows us to reconstruct the optimal shape and to compute the remaining variance of the statistical model from partial information. The statistical model, with its remaining variance, can then be used to constrain the shape space, which is a more efficient shape update as compared to a regularization-based shape model reconstruction. The reconstructed shape is optimized over an edge weighted unsigned distance map calculated from the current segmentation, and is then used as a shape prior for the next iteration of the segmentation. We show the robustness to high-density imaging artifacts of the proposed method by providing a quantitative and qualitative evaluation to the challenging problem of 3D masseter muscles segmentation from CT datasets.