SSGNet: Selective Multi-Scale Receptive Field and Kernel Self-Attention Based on Group-Wise Modality for Brain Tumor Segmentation

Medical image processing has been used in medical image analysis for many years and has achieved great success. However, one challenge is that medical image processing algorithms ineffectively utilize multi-modality characteristics to further extract features. To address this issue, we propose SSGNet based on UNet, which comprises a selective multi-scale receptive field (SMRF) module, a selective kernel self-attention (SKSA) module, and a skip connection attention module (SCAM). The SMRF and SKSA modules have the same function but work in different modality groups. SMRF functions in the T1 and T1ce modality groups, while SKSA is implemented in the T2 and FLAIR modality groups. Their main tasks are to reduce the image size by half, further extract fused features within the groups, and prevent information loss during downsampling. The SCAM uses high-level features to guide the selection of low-level features in skip connections. To improve performance, SSGNet also utilizes deep supervision. Multiple experiments were conducted to evaluate the effectiveness of our model on the BraTS2018 dataset. SSGNet achieved Dice coefficient scores for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) of 91.04, 86.64, and 81.11, respectively. The results show that the proposed model achieved state-of-the-art performance compared with more than twelve benchmarks.