The diagnosis of brain cancer relies heavily on medical imaging techniques,
with MRI being the most commonly used. It is necessary to perform automatic
segmentation of brain tumors on MRI images. This project intends to build an
MRI algorithm based on U-Net. The residual network and the module used to
enhance the context information are combined, and the void space convolution
pooling pyramid is added to the network for processing. The brain glioma MRI
image dataset provided by cancer imaging archives was experimentally verified.
A multi-scale segmentation method based on a weighted least squares filter was
used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of
three-dimensional reconstruction is further improved. Experiments show that the
local texture features obtained by the proposed algorithm are similar to those
obtained by laser scanning. The algorithm is improved by using the U-Net method
and an accuracy of 0.9851 is obtained. This approach significantly enhances the
precision of image segmentation and boosts the efficiency of image
classification.

这篇研究论文介绍了一种基于 U-Net 的 MRI 算法，结合了残差网络和用于增强上下文信息的模块，还添加了空洞卷积池化金字塔用于处理。实验证明了该算法在诊断脑胶质瘤的 MRI 图像分割中取得了较高的准确率，并提升了脑肿瘤的三维重建精度，以及图像分类的效率。