A patch-wise deep residual network (PwDRU-Net102) for multimodal MRI brain tumor segmentation

Gliomas are among the most severe types of brain tumors and can be life-threatening without early detection. Accurate and timely segmentation of brain tumors from MRI scans is crucial for effective treatment planning; however, it remains challenging due to significant variation in tumor shape, size, and location. This paper proposes a 2D Patch-wise Deep Residual U-Net with 102 convolutional layers for automatic tumor segmentation. The approach divides MRI scans into uniform, non-overlapping patches to achieve precise localization and better preserve local features. Residual blocks with identity mapping help mitigate vanishing gradient issues, while dropout layers reduce overfitting during training. T1, T2, and FLAIR modalities from the BraTS 2019 and 2020 datasets were used to evaluate the model. Experimental results show high segmentation accuracy on BraTS 2020 and the Dice Similarity Coefficients (DSC) achieved were 0.9136 (WT), 0.7143 (TC), and 0.7028 (ET). The paper demonstrates that patch-wise deep residual architectures, even with limited training data, can deliver reliable and robust brain tumor segmentation.