Boundary-aware and cross-modal fusion network for enhanced multi-modal brain tumor segmentation

Abstract

In recent years, brain tumor segmentation has emerged as a critical area of focus in medical image analysis. Accurate tumor delineation is essential for effective treatment planning and patient monitoring. Many existing algorithms struggle with accurately delineating complex tumor boundaries, particularly in cases where tumors exhibit heterogeneous features or blend with surrounding healthy tissues. In this paper, I propose a novel boundary-aware multi-modal brain tumor segmentation network, which integrates four key contributions to improve segmentation accuracy. First, I introduce a Boundary Extraction Module (BEM) to capture essential boundary information for segmentation. Second, I present a Boundary Guidance Module (BGM) to guide the segmentation process by incorporating boundary-specific information. Third, I design a Boundary Supervision Module (BSM) to enhance segmentation accuracy by providing multi-level boundary supervision. Lastly, I propose a Cross-feature Fusion (CFF) that integrates complementary information from different MRI modalities to enhance overall segmentation performance. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods, achieving superior tumor segmentation accuracy across brain tumor segmentation datasets, thereby indicating its potential for clinical applications in neuroimaging.