河北科技大学学报2025，Vol.46Issue(4)：395-404,10.DOI:10.7535/hbkd.2025yx04005
基于图卷积的自适应特征融合MRI脑肿瘤分割方法
Graph convolution-based adaptive feature fusion method for MRI brain tumor segmentation
摘要
Abstract
To address the issues of insufficient global information capture and inadequate deep semantic information fusion in the U-Net model for MRI brain tumor segmentation,a novel brain tumor segmentation network,ASGU-Net was proposed.The algorithm was based on 3D U-Net,incorporating a graph convolution inference module to capture additional long-range contextual features.Additionally,dynamic snake convolution(DSConv)was introduced in the encoder-decoder to better accommodate the varied shapes of tumors,enhancing edge feature extraction and improving segmentation accuracy.Furthermore,an adaptive spatial feature fusion(ASFF)module was introduced in the decoder to enhance the feature fusion effect by integrating semantic information captured by multiple encoder blocks.The evaluation on the publicly available BraTS 2019-2021 datasets shows that the Dice values for whole tumor,tumor core and enhanced tumor are 90.70%/90.70%/91.00%,84.90%/84.00%/88.80%and 77.30%/77.40%/82.50%,respectively.The experimental results demonstrate the effectiveness of ASGU-Net in the brain tumor segmentation task.ASGU-Net can effectively addresses the issues of inadequate global information capture and feature fusion,providing effective reference for high-precision automated brain tumor segmentation.关键词
计算机神经网络/脑肿瘤分割/三维U-Net/图卷积推理瓶颈层/动态蛇形卷积/自适应空间特征融合Key words
computer neural network/brain tumor segmentation/3D U-Net/graph convolution inference bottleneck layer/dynamic snake convolution/adaptive spatial feature fusion分类
信息技术与安全科学引用本文复制引用
张野,张睦卿,袁学刚,牛大田..基于图卷积的自适应特征融合MRI脑肿瘤分割方法[J].河北科技大学学报,2025,46(4):395-404,10.基金项目
国家自然科学基金(12172086) （12172086）
辽宁省教育厅基本科研项目(JYTMS20231805) （JYTMS20231805）
