现代电子技术2026，Vol.49Issue(11)：65-71,7.
基于背景激活抑制的弱监督脑肿瘤图像分割算法
Weakly supervised brain tumor image segmentation algorithm based on background activation suppression
摘要
Abstract
Magnetic resonance imaging(MRI)is a commonly used medical technique for brain tumor detection.The accurate brain tumor segmentation is crucial for assessing patient conditions and formulating treatment plans.This paper proposes a background activation suppression mechanism to deal with the excessive background activation caused by unclear subject matter in brain tumor images.Firstly,a classifier is trained to extract the features.Secondly,a background activation suppression module is designed and embedded into the backbone network of the classifier.This module effectively adjusts attention from background regions to foreground regions,and a preliminary segmentation mask is obtained.Finally,a boundary refinement module is introduced.The boundaries of segmentation masks are further refined by calculating affinities from both RGB image features and spatial location information,so that a more precise segmentation is obtained.The proposed method is evaluated on four modalities of the BraTS 2021 dataset and MSD Brain dataset.On the T2-FLAIR modality of the BraTS 2021 dataset,its Dice coefficient and intersection over union(IoU)reach 0.845 and 0.788,respectively.These results are 6.0%and 7.2%higher than those of the Cfd-CAM,respectively,outperforming the existing algorithms.The algorithm presented in this paper improves the accuracy of brain tumor segmentation.It provides valuable ideas for other weakly supervised semantic segmentation(WSSS)algorithms for medical images.关键词
弱监督语义分割/医学MRI图像/肿瘤分割/类激活映射/背景激活抑制/注意力机制/边界细化/亲和度Key words
weakly supervised semantic segmentation/medical MRI image/tumor segmentation/class activation mapping/background activation suppression/attention mechanism/boundary refinement/affinity分类
信息技术与安全科学引用本文复制引用
刘晴晴,李丽宏,赵伟康,滕沛衔,曾紫微,赵邹菲..基于背景激活抑制的弱监督脑肿瘤图像分割算法[J].现代电子技术,2026,49(11):65-71,7.基金项目
河北省自然科学基金项目(F2023402011) （F2023402011）
