An Innovative Approach to Multimodal Brain Tumor Segmentation: The Residual Convolution Gated Neural Network and 3D UNet Integration

Background: The segmentation of brain tumor images remains a challenging yet vital aspect of medical image analysis. In particular, Gliomas, being the most prevalent type of malignant brain tumors, necessitate early and accurate diagnosis for effective monitoring, analysis, and planning of radiotherapy. However, the segmentation of Glioma images presents two major obstacles: the imbalance of segmented classes and a dearth of training data. Thus, the conception of a dependable and suitable model for such medical image segmentation is paramount. Methods: In recent years, the utilization of deep neural networks for the automatic segmentation of Gliomas proved to be very promising. Drawing inspiration from this success, a three-dimensional brain tumor analysis approach, termed as the Residual Convolution Gated Neural Network, which incorporates residual units and signal gating into UNet, thereby enhancing the performance of brain tumor segmentation. By combining the advantages of UNet, ResNet and signal gating, we obtain a novel 3D UNet, featuring block modifications using the newly formulated ResNet 'M' blocks. As a result, our developed Res-Gated-3DUNet network offered improved accuracy in Gliomas image segmentation results. The model was trained and validated using the 2020 Multi -modal Brain Tumor Segmentation Challenge (BraTS2020) datasets. The validation stage using the BraTS2020 dataset resulted in relatively high dice values. The segmentation accuracy was also increased through the incorporation of an innovative combined loss function, proposed in this work. The experimental results reveal that the Res-Gated-3DUNet outperforms conventional brain tumor segmentation algorithms. Conclusion: when compared with current literature, the proposed methodology offers more accurate and efficient 3D brain tumor segmentation. Moreover, the Res-Gated-3DUNet architecture proved to be not only an effective, but also a promising technique for Glioma fine segmentation in multimodal images.