UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
Background: Brain tumor segmentation has a significant impact on the
diagnosis and treatment of brain tumors. Accurate brain tumor segmentation
remains challenging due to their irregular shapes, vague boundaries, and high
variability. Objective: We propose a brain tumor segmentation method that
combines deep learning with prior knowledge derived from a region-growing
algorithm. Methods: The proposed method utilizes a multi-scale feature fusion
(MSFF) module and adaptive attention mechanisms (AAM) to extract multi-scale
features and capture global contextual information. To enhance the model's
robustness in low-confidence regions, the Monte Carlo Dropout (MC Dropout)
strategy is employed for uncertainty estimation. Results: Extensive experiments
demonstrate that the proposed method achieves superior performance on Brain
Tumor Segmentation (BraTS) datasets, significantly outperforming various
state-of-the-art methods. On the BraTS2021 dataset, the test Dice scores are
89.18% for Enhancing Tumor (ET) segmentation, 93.67% for Whole Tumor (WT)
segmentation, and 91.23% for Tumor Core (TC) segmentation. On the BraTS2019
validation set, the validation Dice scores are 87.43%, 90.92%, and 90.40% for
ET, WT, and TC segmentation, respectively. Ablation studies further confirmed
the contribution of each module to segmentation accuracy, indicating that each
component played a vital role in overall performance improvement. Conclusion:
This study proposed a novel 3D brain tumor segmentation network based on the
U-Net architecture. By incorporating the prior knowledge and employing the
uncertainty estimation method, the robustness and performance were improved.
The code for the proposed method is available at
https://github.com/chenzhao2023/UPMAD_Net_BrainSeg.
