DCEF-AVNet: multi-scale feature fusion and attention mechanism-guided brain tumor segmentation network.

Abstract

Purpose: Accurate and efficient automatic segmentation of brain tumors is critical for diagnosis and treatment. However, the diversity in the appearance, location, and shape of brain tumors and their subregions, coupled with complex boundaries, presents significant challenges. We aim to improve segmentation accuracy by addressing limitations in V-Net, including insufficient utilization of multi-scale features and difficulties in managing complex spatial relationships and long-range dependencies.

Approach: We propose an improved network structure, dynamic convolution enhanced fusion axial V-Net (DCEF-AVNet), which integrates an enhanced feature fusion module and axial attention mechanisms. The feature fusion module integrates dynamic convolution with a redesigned skip connection strategy to effectively combine multi-scale features, reducing feature inconsistencies and improving representation capability. Axial attention mechanisms are introduced at encoder-decoder connections to manage spatial relationships and alleviate long-range dependency issues. The network was evaluated using the BraTS2021 dataset, with performance measured in terms of Dice coefficients and Hausdorff distances.

Results: DCEF-AVNet achieved Dice coefficients of 92.49%, 91.35%, and 91.96% for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) regions, respectively, significantly outperforming baseline methods. The model also demonstrated robust performance across multiple runs, with consistently low standard deviations in metrics.

Conclusions: The integration of dynamic convolution, enhanced feature fusion, and axial attention mechanisms enables DCEF-AVNet to deliver superior segmentation accuracy and robustness. These results underscore its potential for advancing automated brain tumor segmentation and improving clinical decision-making.