Label inference encoded with local and global patch priors
In this paper, a novel label inference method encoded with local and global patch priors is introduced for the segmentation of subcortical structures in brain MR images. Due to the serious overlap of intensity profiles among different tissues in brain MR images, the conventional patch prior estimated with similarity measurement can be adversely impacted and become misleading during the final label inference procedure. As such, to obtain a more discriminative patch representation, we propose to capture local patch prior using sparse learning. Besides the local and low-level patch prior, the high-level structural properties of each subcortical structure are also taken into consideration and global patch prior is extracted with Convolutional Neural Networks. Experiments have been carried out on two publicly available datasets and results indicate that the proposed method can obtain the best performance as compared with other state-of-the-art methods.