Artificial identification of ischemic stroke lesion is time-consuming, laborious and easy be added subjective differences. To solve this problem, an automatic segmentation algorithm based on 3D deep residual network and cascade U-Net was proposed. Firstly, in order to efficiently utilize 3D contextual information of the image and the solve class imbalance issue, the patches were extracted from the stroke Magnetic Resonance Image (MRI) and put into network. Then, a segmentation model based on 3D deep residual network and cascade U-Net was used to extract features of the image patches, and the coarse segmentation result was obtained. Finally, the fine segmentation process was used to optimize the coarse segmentation result. The experiment results show that, on the dataset of Ischemic Stroke LEsion Segmentation (ISLES), for the proposed algorithm, the Dice similarity coefficient reached 0.81, the recall reached 0.81 and the precision reached 0.81, the distance coefficient Average Symmetric Surface Distance (ASSD) reached 1.32 and Hausdorff Distance (HD) reached 22.67. Compared with 3D U-Net algorithm, level set algorithm, Fuzzy C-Means (FCM) algorithm and Convolutional Neural Network (CNN) algorithm, the proposed algorithm has better segmentation performance.

Neural networks have strong nonlinear learning ability, so the super-resolution algorithms based on neural networks are preliminarily studied. These algorithms can only be used in controlled microscanning, which has uniform displacement between frames. It is difficult to apply these algorithms to uncontrolled microscanning. In order to overcome the limiting condition and obtain better super-resolution performance, a deblurring algorithm using Radial Basis Function (RBF) neural network was firstly proposed, which was then combined with non-uniform interpolation step to form a new two-step super-resolution algorithm. The simulation results show that the Structural SIMilarity (SSIM) index of proposed algorithm is 0.55-0.7. The proposed two-step super-resolution algorithm not only extends application scope of RBF neural network but also achieves good super-resolution performance.