Integrating Image Super-Resolution Network and Semantic Segmentation for 3D Reconstruction of Medical Sequence Image

The three-dimensional model of medical sequence image can display human tissues and organs in stereo, and effectively overcome the insufficiency of two-dimensional examination results for disease diagnosis. In this paper, the three-dimensional reconstruction of sequential medical images is studied from the perspective of image super resolution and semantic segmentation. In view of the problem of ignoring global information association in existing image super-resolution reconstruction methods and the problem of long distance dependence due to lack of information in existing image semantic segmentation methods. In this paper, the method of fusion of global and local features is introduced into the super-resolution reconstruction task, and the method of cross-level fusion compensation is introduced into the semantic segmentation task of medical images, so as to realize the three-dimensional reconstruction of sequential medical images. Finally, the reliability of the proposed method is verified by experiments. In the experiment of 3D reconstruction of medical sequence images, we can observe that some details are preserved completely and the clarity is improved significantly after reconstruction.