A 3D Dual Encoder Mirror Difference ResU-Net for Multimodal Brain Tumor Segmentation

Brain tumors are characterized by their relatively high incidence and mortality rates, highlighting the utmost importance of precise automatic segmentation for subsequent diagnosis and treatment. Although deep learning has significantly advanced the field of accurate and efficient automatic brain tumor segmentation, several challenges still persist. In this paper, we introduce a novel architecture called the Dual Encoder Mirror Difference Residual U-Net (DEMD-ResUNet). This approach incorporates dual encoders that process both the original and horizontally flipped images. Additionally, residual blocks are employed to substitute the original convolutional blocks in the encoder section of the U-Net structure. This modification not only streamlines network training but also mitigates issues related to network degradation and the loss of detailed information. To further enhance feature representation, we propose a Multimodal Difference Feature Augmentation (MDFA) module, which effectively highlights abnormal regions in both the original and mirrored brain tumor images to facilitate better feature discrimination. Moreover, a Mirror Difference Feature Fusion (MDFF) module is integrated between the dual encoders and the decoder. This module efficiently transfers features from both the original and mirrored images to the decoder, leveraging the symmetrical information inherent in the images and subsequently boosting the segmentation performance of the model. Ablation experiments conducted on the DEMD-ResUNet model demonstrate the efficacy of its various modules and hyperparameter settings. When evaluated on the BraTS 2018 and BraTS 2019 datasets, our model achieves impressive Dice similarity coefficient (DSC) values of 0.862, 0.925, and 0.905 for Enhanced tumor (ET), Whole tumor (WT), and Tumor core (TC) in the former, and 0.869, 0.922, and 0.916 in the latter, respectively.