现代电子技术2024，Vol.47Issue(17)：47-52,6.DOI:10.16652/j.issn.1004-373x.2024.17.008
融合共享Net的跨模态脑肿瘤分割方法
Cross-modal brain tumor segmentation method based on fusion shared Net
摘要
Abstract
A cross-modal segmentation framework based on shared Net is proposed to enhance the generalization ability of the brain tumor image segmentation algorithm.The framework consists of three stages,including style conversion,cross-domain training and adaptive discrimination.The Bézier curve is used for the domain variation,and the invisible target domain is simulated based on a variety of images whose grayscales are different from the source domain.A shared Net model based on the lightweight scale attention module is constructed,and multiple styles of gray images are input into the shared Net to learn the weight information of different domains.The optimal segmentation results are selected adaptively by an adaptive discriminator during model inference.Simulation results show that the proposed shared Net algorithm can achieve effective generalization,which is superior to the most advanced methods in segmentation performance and computational efficiency.关键词
U-Net/医学图像分割/脑肿瘤/跨模态/域泛化/贝塞尔曲线Key words
U-Net/medical image segmentation/brain tumor/cross-mode/domain generalization/Bézier curve分类
电子信息工程引用本文复制引用
李志刚,张艺荣..融合共享Net的跨模态脑肿瘤分割方法[J].现代电子技术,2024,47(17):47-52,6.基金项目
河北省高等学校科学技术研究项目(ZD2021088) （ZD2021088）
河北省海洋生态修复与智慧海洋工程研究中心开放基金项目(HBMESO2315) （HBMESO2315）
