GMDNet: Grouped Encoder-Mixer-Decoder Architecture Based on the Role of Modalities for Brain Tumor MRI Image Segmentation

Abstract

Although deep learning has significantly advanced brain tumor MRI segmentation and preoperative planning, existing methods like U-Net and Transformer, which are widely used Encoder–Decoder architectures in medical image segmentation, still have limitations. Specifically, these methods fail to fully leverage the unique characteristics of different MRI modalities during the feature extraction stage, thereby hindering further improvements in segmentation accuracy. Currently, MRI modalities are typically treated as independent entities or as uncorrelated features during feature extraction, neglecting their potential interdependencies. To address this gap, we introduce the GMD architecture (Grouped Encoder-Mixer-Decoder), which is designed to enhance information capture during feature extraction by considering the intercorrelation and complementary nature of different modalities. In the proposed GMD architecture, input images are first grouped by modality in the grouped encoder based on a modality-specific strategy. The extracted features are then fused and optimized in the mixer module, and the final segmentation is achieved through the decoder. We implement this architecture in GMDNet to validate its effectiveness. Experiments demonstrate that GMDNet not only achieves outstanding performance under complete modality conditions but also maintains robust performance even when certain modalities are missing. To further enhance performance in incomplete modality, we propose an innovative reuse modality strategy that significantly improves segmentation accuracy compared to conventional approaches. We evaluated the performance of GMDNet on the BraTS 2018 and BraTS 2021 datasets. Under complete modality conditions, GMDNet achieved Dice scores of 91.21%, 87.11%, 80.97%, and 86.43% for WT (Whole Tumor), TC (Tumor Core), ET (Enhancing Tumor) and Average on the BraTS 2018, and 91.87%, 87.25%, 83.16%, and 87.42% on the BraTS 2021. Under incomplete modality conditions, when T1, T1ce, T2, and FLAIR were missing, the Dice scores on the BraTS 2021 dataset were 86.47%, 73.29%, 86.46%, and 82.54%, respectively. After applying the reuse modality strategy, the scores improved to 87.17%, 75.07%, 86.91%, and 86.22%. Overall, extensive experiments demonstrate that proposed GMDNet architecture achieves state-of-the-art performance, outperforming the compared models of this paper in complete or incomplete modality.