Fast and Unsupervised Non-Local Feature Learning for Direct Volume Rendering of 3D Medical Images

To improve the efficiency of medical visualization for computer aided surgery, we propose a fast and unsupervised 3D-CNN based non-local feature learning network. The proposed network consists of an encoder structure and a decoder structure. The encoder of the network projects the cube into a high-dimensional feature space, and the decoder of the network reconstructs the cube from the feature space. The decoder of the network serves as a dictionary shared by the cube to enforce the features for similar parts to be similar although they may distribute at disjointed locations. With such structures, the network is able to extract non-local features of the entire data. Moreover, a sparse constraint is incorporated into the network to increase the discriminative of the non-local features. Then the extracted non-local features of each voxel are fused with the corresponding position matrix and Hessian matrix for the voxel classification using Random Forest. Finally, a multidimensional transfer function is designed to enable the volume rendering. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with much less training time.