A conflict-free multi-modal fusion network with spatial reinforcement transformers for brain tumor segmentation.

IF 7 2区 医学 Q1 BIOLOGY Computers in biology and medicine Pub Date : 2024-12-01 Epub Date: 2024-11-05 DOI:10.1016/j.compbiomed.2024.109331
Tianyun Hu, Hongqing Zhu, Ziying Wang, Ning Chen, Bingcang Huang, Weiping Lu, Ying Wang
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Abstract

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.

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用于脑肿瘤分割的无冲突多模态融合网络与空间增强变换器。
脑胶质瘤是全球癌症死亡的主要原因。现有的使用多模态输入的胶质瘤分割方法往往依赖于将所有模态的图像堆叠在一起的简单方法,而忽略了可优化诊断结果的特定模态特征。本文介绍的 STE-Net 是一种基于空间增强混合变压器的三分支多模态证据融合网络,设计用于无冲突脑肿瘤分割。STE-Net 有两个独立的编码器-解码器分支,分别处理不同的模态集,另外还有一个分支通过跨模态信道融合(CMCF)模块整合特征。编码器采用空间增强混合变换器(SRHT),将斯温变换器模块和改进的卷积模块相结合,以捕捉更丰富的空间信息。在输出层面,利用 Dempster-Shafer(D-S)证据理论和复杂网络框架内的冲突解决策略,开发了无冲突证据融合机制(CEFM)。该机制确保了三个输出头之间的平衡可靠性,并缓解了潜在冲突。每个输出都被视为复杂网络中的一个节点,通过计算直接和间接权重来重新评估其重要性,以防止潜在的相互冲突。我们在三个公共数据集上对 STE-Net 进行了评估:BraTS2018、BraTS2019 和 BraTS2021。定性和定量结果都表明,STE-Net 优于几种最先进的方法。统计分析进一步证实了预测肿瘤与地面实况之间的强相关性。该项目的代码见 https://github.com/whotwin/STE-Net。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers in biology and medicine
Computers in biology and medicine 工程技术-工程:生物医学
CiteScore
11.70
自引率
10.40%
发文量
1086
审稿时长
74 days
期刊介绍: Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.
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