SGAFNet: Robust brain tumor segmentation via learnable sequence-guided adaptive fusion in available MRI acquisitions

Automatic and accurate segmentation of brain tumors from Magnetic Resonance Imaging (MRI) data holds significant promise for advancing clinical applicability. However, substantial challenges persist in algorithm development, particularly in scenarios where MRI sequences are incomplete or missing. Although recent automatic segmentation methods have demonstrated notable progress in addressing incomplete sequence scenarios, they often overlook the varying contributions of different MRI sequences to the final segmentation. To address this limitation, we propose a Learnable Sequence-Guided Adaptive Fusion Network (SGAFNet) for robust brain tumor segmentation under incomplete sequence scenarios. Our architecture features parallel encoder-decoders for sequence-specific feature extraction, enhanced by two novel components: (1) a Learned Sequence-Guided Weighted Average (SGWA) module, which adaptively fuses different sequence features by learning sequence-specific contribution factors based on embedded priors, and (2) a Sequence-Specific Attention (SSA) module, which establishes cross-sequence dependencies between available sequences feature and the fused features generated by the SGWA. Comprehensive experiments on the BraTS2018 and BraTS2020 datasets demonstrate that our framework achieves state-of-the-art performance in handling incomplete sequences scenarios compared to existing approaches, with ablation studies confirming the critical role of the proposed SGWA and SSA modules. Increased robustness to incomplete MRI acquisitions enhances clinical applicability, facilitating more consistent diagnostic workflows.