Improved Bayesian Brain MR Image Segmentation by Incorporating Subspace-Based Spatial Prior into Deep Neural Networks

Accurate segmentation of brain tissues is important for brain imaging applications. Learning the high-dimensional spatial-intensity distributions of brain tissues is challenging for classical Bayesian classification and deep learning-based methods. This paper presents a new method that synergistically integrate a tissue spatial prior in the form of a mixture-of-eigenmodes with deep learning-based classification. Leveraging the spatial prior, a Bayesian classifier and a cluster of patch-based position-dependent neural networks were built to capture global and local spatial-intensity distributions, respectively. By combining the spatial prior, Bayesian classifier, and the proposed networks, our method significantly improved the segmentation performance compared with the state-of-the-art methods.