W-AGRU-Net: a dual-stream attention framework for robust brain tumor segmentation in clinical MRI imaging

Abstract

Segmenting brain tumors from MRI scans is a challenging aspect of medical image analysis because of anatomical complexity, ambiguous tumor boundaries, and variability of shape. In this paper, we propose W-AGRU-Net, a new dual-stream U-Net framework that combines W-Attention mechanisms with residual connections for strong and accurate glioma segmentation. Our framework uses two asymmetric streams that have pyramid-shaped dilated convolution schemes (PDCS) for hierarchical multi-scale context capturing. The framework also includes residual units that utilize Squeeze-and-Excitation (SE) blocks for channel-wise recalibration of features across multiple resolutions, thereby strengthening relevant tumor regions while reducing noise. The method is thoroughly evaluated on the TCIA LGG Segmentation and Figshare datasets, where it outperformed the state-of-the-art approaches. On the Figshare dataset, the approach achieved best-in-class metrics, including a registration Dice coefficient of 97.87% and Jaccard index of 97.44%, as well as 95.14% precision and 93.42% sensitivity. On the TCIA dataset, we achieved 94.0% Dice, 99.9% pixel-level accuracy, 89.29% Jaccard index, and 90.01% sensitivity, demonstrating improved performance over baseline methods. Concerning more recent state-of-the-art approaches with Dice scores of 92.0% (TCIA) and 96.9% (Figshare), our approach showed enhanced accuracy in brain tumor segmentation, and we expect our approach to work well for clinical brain imaging applications.