| For medical image segmentation tasks,the traditional manual method relies on the doctor’s experience and knowledge,which is not only labor intensive,but also the segmentation accuracy is not guaranteed.With the development of computer technology,the automatic segmentation method based on deep learning has shown its own unique advantages in various fields,so there is a technology that combines deep learning and medical images to realize automatic medical image segmentation.In this context,this thesis adopts deep learning as a tool to explore the application of generative adversarial networks in brain tumor segmentation.The main work is as follows:(1)A parallel multi-scale-based generative adversarial network model is implemented from the perspective of multi-scale features.This network is a multi-scale parallel structure with an attention mechanism and multiple residual blocks,which can alleviate the problems of gradient dispersion and network failure that appear in the process of continuous deepening of the network depth.It allows the network to extract the feature map with different focus from the input data at the same time.And multi-scale inference fusion helps the network model to improve the sensitivity of detailed information and the segmentation accuracy of different regions of brain tumors.(2)The fine-grained extraction module(FEM)is implemented in the process of studying how to improve the accuracy of the generative adversarial network for segmentation of small targets.This module can reduce unnecessary loss of semantic information in the coding process to a certain extent.And more fine-grained information can be extracted in-depth to strengthen the geometric constraints of local and global information.These characteristics make it possible to have more refined features in the deep neural network to describe the target area and improve the performance of the network model in related tasks.(3)A novel two-stage generative adversarial network To Sta GAN is implemented from the perspective of staged processing,which aims to solve the problem of information loss in the network model and is applied to the field of brain tumor segmentation.The model constructs a process from coarse-to-fine in the process of segmentation,which firstly to generate coarse segmentation results in the one stage network.And in the second stage,the coarse results are optimized to gradually achieve a fine result,which is under the guidance of features obtained from the proposed fine-grained extraction module. |
