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