MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation

Abstract

Introduction Medical image segmentation is essential for analyzing medical data, improving diagnostics, treatment planning, and research. However, current methods struggle with different imaging types, poor generalization, and rare structure detection. Methods To address these issues, we propose MedFusion-TransNet, a novel multi-modal fusion approach utilizing transformer-based architectures. By integrating multi-scale feature encoding, attention mechanisms, and dynamic optimization, our method significantly enhances segmentation precision. Our method uses the Context-Aware Segmentation Network (CASNet) and Dynamic Region-Guided Optimization (DRGO) to enhance segmentation by focusing on key anatomical areas. Results These innovations tackle challenges like imbalanced datasets, boundary delineation, and multi-modal complexity. Validation on benchmark datasets demonstrates substantial improvements in accuracy, robustness, and boundary precision, marking a significant step forward in segmentation technologies. Discussion MedFusion-TransNet offers a transformative tool for advancing the quality and reliability of medical image analysis across diverse clinical applications.