Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net
Maryann M. Gitonga
This paper proposes a 3D attention-based U-Net architecture for multi-region
segmentation of brain tumors using a single stacked multi-modal volume created
by combining three non-native MRI volumes. The attention mechanism added to the
decoder side of the U-Net helps to improve segmentation accuracy by
de-emphasizing healthy tissues and accentuating malignant tissues, resulting in
better generalization power and reduced computational resources. The method is
trained and evaluated on the BraTS 2021 Task 1 dataset, and demonstrates
improvement of accuracy over other approaches. My findings suggest that the
proposed approach has potential to enhance brain tumor segmentation using
multi-modal MRI data, contributing to better understanding and diagnosis of
brain diseases. This work highlights the importance of combining multiple
imaging modalities and incorporating attention mechanisms for improved accuracy
in brain tumor segmentation.