Abstract
Multi-modal brain tumors segmentation is a critical step for diagnosing and monitoring brain-related disease. Many studies have developed models for this task, but two challenges remain, i.e., weak feature aggregation and poorly segmented edges. To address these issues, we develop an improved Diffusion model with relation-aware attention and edge-aware constraint, namely Diff-RE, for multi-modal brain tumor segmentation. Specifically, the volume data and noisy segmentation label map are paralleled fed into encoder module to extract high-level features. During training, weights are shared to ensure consistency. Then, extracted features are channel-wise concatenated and passed through the relation-aware attention module, which enhances appearance features using global structural relationships. Finally, the decoder module processes the attention-enhanced features to generate segmentation results. To improve boundary accuracy, an edge-aware constraint module is introduced during training. Our framework is trained and evaluated using three benchmark datasets, i.e., BraTS 2018, 2019, and 2020. Experimental results demonstrate that Diff-RE is effective and highlight its superiority over peer methods.