---
tags:
- semantic segmentation
- brain tumor segmentation
library_name: tf
datasets:
- https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1
metrics:
- Dice Coefficient
- Tversky Loss
pipeline_tag: image-segmentation
license: cc-by-sa-4.0
---
# 3D Attention-based UNet for Multi-modal Brain Tumor Segmentation

## Model Description
This model uses the UNet architecture, which employs a contracting path to down-sample image dimensions and an expanding path to up-sample while retaining spatial information through skip connections. 
3D attention gates are introduced to generate 3D channel and spatial attention by utilizing 3D inter-channel and inter-spatial feature relationships.
The input is a combined scan of 3 modalities (T1CE, T2 and T2-FLAIR) with the dimensions: 3 x L x W x no. of slices. 
The model attained a Dice Coefficient score of 0.9562 and a Tversky Loss of 0.0438

## Dataset Description
The BRaTs (Brain Tumor Segmentation) 2021 Dataset, consisting of 1400 multi-parametric MRI (mpMRI) scans with expert neuro-radiologists' ground truth annotations, was used for this project. 
The dataset provides mpMRI scans in NIfTI format and includes native (T1), post-contrast T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, along with manually annotated GD-enhancing tumor, peritumoral edematous/invaded tissue, necrotic tumor core, and normal tissue.
Checkout the dataset [here](https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1).

## Model Notebook
Find the notebook containing the model code on [my Github](https://github.com/MaryannGitonga/Brain-Tumor-Segmentation-Using-3D-Attention-Based-UNet).