Brain tumor segmentation network and segmentation method based on U-Net network

Abstract

The invention discloses a brain tumor segmentation network and segmentation method based on a U-Net network. The tail of a contraction path of the segmentation network is connected with a spatial pyramid pooling structure; hole convolution of different scales is introduced into a network jump connection part of the segmentation network; an Add operation and original input are adopted to form a residual block with hole convolution; a receptive field of shallow feature information in the contraction path is expanded; fusing with an expansion path of a corresponding stage is carried out. The segmentation method comprises the following steps: cutting and preprocessing a training data set, then constructing a brain tumor segmentation network DCU-Net based on a U-Net network, then inputting a preprocessed two-dimensional image into a segmentation model for feature learning and optimization, obtaining an optimal parameter model of the segmentation model, and finally inputting a to-be-segmented test data set image into the segmentation model for tumor region segmentation. According to the method, the problems of over-segmentation and under-segmentation in brain tumor segmentation can be effectively solved, and the brain tumor segmentation precision is improved.

Description

technical field [0001] The present invention relates to the technical field of neural network and hole convolution, in particular to a U-Net network-based brain tumor segmentation network and segmentation method. Background technique [0002] Gliomas are one of the most common primary tumors in the brain. They grow from glioma cells and can be divided into low-grade gliomas and high-grade gliomas. High-grade gliomas (HGG) are more aggressive to patients and have a life expectancy of up to two years, while low-grade gliomas (LGG) are benign or malignant and grow slower in patients with a life expectancy of several years. Benign tumors can generally recover after surgical treatment, while malignant tumors are difficult to cure due to their intractability and seriously endanger human life and health. Therefore, how to better diagnose and treat malignant tumors is very important. [0003] With the development of medical imaging technology, imaging diagnosis plays an increasingl...

AI Technical Summary

Problems solved by technology

[0012] The researchers sliced ​​the input 3D data by building a 2D network structure and converted it into 2D data input, so that the network can complete the rapid training of network parameters on ordinary hardware facilities, and building a 3D network structure can make full use of The three-dimensional features of MR images can obtain more accurate segmentation results. However, due to the large number of parameters in the training network, it is difficult to train the network from scratch, and there are problems such as excessive consumption of GPU and memory, and the requirements for computer hardware facilities are high.

Images