文献类型：期刊文献

英文题名：A conflict-free multi-modal fusion network with spatial reinforcement transformers for brain tumor segmentation

作者：Hu, Tianyun[1]; Zhu, Hongqing[1]; Wang, Ziying[1]; Chen, Ning[1]; Huang, Bingcang[2]; Lu, Weiping[2]; Wang, Ying[3]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Radiology, Gongli Hospital of Shanghai Pudong New Area, Shanghai, 200135, China; [3] Shanghai Health Commission Key Lab of Artificial Intelligence [AI]-Based Management of Inflammation and Chronic Diseases, Sino-French Cooperative Central Lab, Gongli Hospital of Shanghai Pudong New Area, Shanghai, 200135, China

年份：2024

卷号：183

外文期刊名：Computers in Biology and Medicine

收录：EI(收录号：20244517324062)

语种：英文

外文关键词：Encoding (symbols) - Image coding - Image fusion - Image segmentation - Magnetic resonance imaging - Network coding - Nuclear magnetic resonance

摘要：Brain gliomas are a leading cause of cancer mortality worldwide. Existing glioma segmentation approaches using multi-modal inputs often rely on a simplistic approach of stacking images from all modalities, disregarding modality-specific features that could optimize diagnostic outcomes. This paper introduces STE-Net, a spatial reinforcement hybrid Transformer-based tri-branch multi-modal evidential fusion network designed for conflict-free brain tumor segmentation. STE-Net features two independent encoder–decoder branches that process distinct modality sets, along with an additional branch that integrates features through a cross-modal channel-wise fusion (CMCF) module. The encoder employs a spatial reinforcement hybrid Transformer (SRHT), which combines a Swin Transformer block and a modified convolution block to capture richer spatial information. At the output level, a conflict-free evidential fusion mechanism (CEFM) is developed, leveraging the Dempster–Shafer (D–S) evidence theory and a conflict-solving strategy within a complex network framework. This mechanism ensures balanced reliability among the three output heads and mitigates potential conflicts. Each output is treated as a node in the complex network, and its importance is reassessed through the computation of direct and indirect weights to prevent potential mutual conflicts. We evaluate STE-Net on three public datasets: BraTS2018, BraTS2019, and BraTS2021. Both qualitative and quantitative results demonstrate that STE-Net outperforms several state-of-the-art methods. Statistical analysis further confirms the strong correlation between predicted tumors and ground truth. The code for this project is available at https://github.com/whotwin/STE-Net. ? 2024