# Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net

arXiv: https://arxiv.org/abs/2305.06203 · HTML: https://mlsanity.com/paper/2305.06203/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet](https://mlsanity.com/paper/2104.00985/) (2021)
- [E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge](https://mlsanity.com/paper/2110.02519/) (2021)

## Built on by (most cited first, 1 of 1)

- [Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI](https://mlsanity.com/paper/2409.17273/) (2024)

## Abstract (arXiv)

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.