A Gabor-enhanced deep learning approach with dual-attention for 3D MRI brain tumor segmentation.

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Abstract

Robust 3D brain tumor MRI segmentation is significant for diagnosis and treatment. However, the tumor heterogeneity, irregular shape, and complicated texture are challenging. Deep learning has transformed medical image analysis by feature extraction directly from the data, greatly enhancing the accuracy of segmentation. The functionality of deep models can be complemented by adding modules like texture-sensitive customized convolution layers and attention mechanisms. These components allow the model to focus its attention on pertinent locations and boundary definition problems. In this paper, a texture-aware deep learning method that improves the U-Net structure by adding a trainable Gabor convolution layer in the input for rich textural feature capture is proposed. Such features are fused in parallel with standard convolutional outputs to better represent tumors. The model also utilizes dual attention modules, Squeeze-and-Excitation blocks in the encoder for dynamically adjusting channel-wise features and Attention Gates for boosting skip connections by removing trivial areas and weighting tumor areas. The working of each module is explored through explainable artificial intelligence methods to ensure interpretability. To address class imbalance, a weighted combined loss function is applied. The model achieves Dice coefficients of 91.62%, 89.92%, and 88.86% for whole tumor, tumor core, and enhancing tumor respectively on BraTS2021 dataset. Large-scale quantitative and qualitative evaluations on BraTS2021, validated on BraTS benchmarks, prove the accuracy and robustness of the proposed model. The proposed approach results are superior to benchmark U-Net and other state-of-the-art segmentation methods, offering a robust and interpretable solution for clinical use.

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