Brain Tumor Segmentation with Multi-Path U-Net with Residual Extended Skip Connections

Early diagnosis of brain tumors is extremely important, and shortening the interval between the acquisition of MRI images and reporting of the results is critical for patients. In the diagnosis of brain tumors, CT and MRI are some of the core diagnostic techniques used today. Our main goal is to reduce the workload of radiologists by developing a neural network that segments MRI images of the brain so we propose a multi-path segmentation algorithm based on U-Net architecture that uses residual extended skip blocks. Our proposed model is trained and tested with Gazi Brains 2020 Dataset. We evaluated the results using the dice similarity coefficient and compared the results with other segmentation algorithms and saw that our proposed model has comparatively better results. Our proposed model is using T1-Weighted, T2-Weighted, and Flair MRI images together as inputs, whereas other segmentation models, are using T2-Weighted or Flair MRI images as input. Implementation of the model and trained models are available at

https://github.com/batuhansozer/brain-segmentation-with-novel-multi-path-model