计算机应用与软件2025，Vol.42Issue(5)：203-208,246,7.DOI:10.3969/j.issn.1000-386x.2025.05.028
基于金字塔结构的神经胶质瘤图像分割模型
A GLIOMA IMAGE SEGMENTATION MODEL BASED ON PYRAMID
摘要
Abstract
To improve the accuracy of Glioma image segmentation and ensure that the edge information of the entire tumor can be obtained more accurately,based on U-shaped convolutional neural network(UNet),a dilated convolutional pyramid model is designed to capture the context of the image at multiple scales to obtain more features.Combined with the efficient channel attention network(ECA)attention module,the model could pay more attention to the most informative channel features,while suppressing those unimportant channel features.The experimental results show that the FLAIR sequence has advantages in segmenting the entire tumor.The intersection over union(IoU)and Dice coefficient(DSC)scores of the designed model in the FLAIR sequence can reach 0.93 and 0.86,respectively,which are 0.07 and 0.05 higher than the original UNet network.It can be concluded that the proposed model effectively obtains more edge information,thereby improving the segmentation accuracy of the entire tumor region in glioma images.关键词
神经胶质瘤/图像分割/UNet/空洞卷积金字塔/ECAKey words
Glioma/Image segmentation/UNet/Dilated convolutional pyramid/ECA分类
信息技术与安全科学引用本文复制引用
夏英茹,陆振宇,詹天明..基于金字塔结构的神经胶质瘤图像分割模型[J].计算机应用与软件,2025,42(5):203-208,246,7.基金项目
国家自然科学基金项目(61773220) （61773220）
江苏省自然科学基金项目(BK20150523). （BK20150523）
