Tianyun Hu, Hongqing Zhu, Ziying Wang, Ning Chen, Bingcang Huang, Weiping Lu, Ying Wang
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引用次数: 0
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.
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.