Multi-attention Mechanism for Enhanced Pseudo-3D Prostate Zonal Segmentation

Abstract

This study presents a novel pseudo-3D Global-Local Channel Spatial Attention (GLCSA) mechanism designed to enhance prostate zonal segmentation in high-resolution T2-weighted MRI images. GLCSA captures complex, multi-dimensional features while maintaining computational efficiency by integrating global and local attention in channel and spatial domains, complemented by a slice interaction module simulating 3D processing. Applied across various U-Net architectures, GLCSA was evaluated on two datasets: a proprietary set of 44 patients and the public ProstateX dataset of 204 patients. Performance, measured using the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD) metrics, demonstrated significant improvements in segmentation accuracy for both the transition zone (TZ) and peripheral zone (PZ), with minimal parameter increase (1.27%). GLCSA achieved DSC increases of 0.74% and 11.75% for TZ and PZ, respectively, in the proprietary dataset. In the ProstateX dataset, improvements were even more pronounced, with DSC increases of 7.34% for TZ and 24.80% for PZ. Comparative analysis showed GLCSA-UNet performing competitively against other 2D, 2.5D, and 3D models, with DSC values of 0.85 (TZ) and 0.65 (PZ) on the proprietary dataset and 0.80 (TZ) and 0.76 (PZ) on the ProstateX dataset. Similarly, MSD values were 1.14 (TZ) and 1.21 (PZ) on the proprietary dataset and 1.48 (TZ) and 0.98 (PZ) on the ProstateX dataset. Ablation studies highlighted the effectiveness of combining channel and spatial attention and the advantages of global embedding over patch-based methods. In conclusion, GLCSA offers a robust balance between the detailed feature capture of 3D models and the efficiency of 2D models, presenting a promising tool for improving prostate MRI image segmentation.

Keywords: Attention variant; Deep learning; Dot product; Feature map; Prostate MRI image segmentation.