# 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).
{"license": "cc-by-sa-4.0", "library_name": "tf", "tags": ["semantic segmentation", "brain tumor segmentation"], "datasets": ["https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1"], "metrics": ["Dice Coefficient", "Tversky Loss"], "pipeline_tag": "image-segmentation"}
tf semantic segmentation brain tumor segmentation image-segmentation license:cc-by-sa-4.0 region:us
TAGS
#tf #semantic segmentation #brain tumor segmentation #image-segmentation #license-cc-by-sa-4.0 #region-us 

# 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.

## Model Notebook
Find the notebook containing the model code on my Github.
# 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. ## Model Notebook
Find the notebook containing the model code on my Github.
TAGS
#tf #semantic segmentation #brain tumor segmentation #image-segmentation #license-cc-by-sa-4.0 #region-us 
 # 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. ## Model Notebook
Find the notebook containing the model code on my Github.
TAGS
#tf #semantic segmentation #brain tumor segmentation #image-segmentation #license-cc-by-sa-4.0 #region-us 
# 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.## Model Notebook
Find the notebook containing the model code on my Github.