Robust shape prior modeling based on Gaussian-Bernoulli restricted Boltzmann Machine

Shape information is essential in medical image analysis as the anatomical structures usually have strong shape characteristics. Shape priors can resolve ambiguities when the low level appearance is weak or misleading due to imaging artifacts and diseases. In this paper, we propose a shape prior model based on the Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM). This powerful generative model is effective in capturing complex shape variations and handling nonlinear shape transformations. The model also shows great robustness, which is able to handle both outliers and Gaussian noise with large variance. We validate our model on synthetic data and a real clinical problem, i.e., lung segmentation in chest X-ray. Experiments show that our shape modeling method is qualitatively and quantitatively better than other widely-used shape prior methods.