Enhanced Deep Animation Video interpolation

Brain tissue segmentation from magnetic resonance imaging (MRI) is of significant importance for clinical application and cognitive research. The promising deep learning based methods heavily depend on the quality and quantity of training datasets, and also ignore the domain knowledge. To overcome this issue, this paper proposes a novel iterative seeded region growing (ISRG) approach for brain tissue segmentation with only one training image needed. After super-voxel generation and matching, we first select the high confidence seeded regions based on the high similarity between individual brain images. Then, we initial the voxel-wise tissue probabilities with a proposed fully convolutional network (named TPUNet). Thirdly, the seeded regions are updated according to the voxel-wise tissue probabilities. The second and the third steps are repeated to iteratively update the seeded regions until the entire image is covered. The proposed approach is evaluated on IBSR18 dataset and achieves better results compared with other methods.