Spread sheet including Results and related works is available in here.

Dataset is available in here.

1. Training: 3D MRI images from 369 patients (Height=240, Width=240, Depth=155)
   • Images: 4 modalities
   • Masks: 4 classes(0, 1, 2, and 4)

2. Validation: 3D MRI images from 125 patients (Height=240, Width=240, Depth=155)
   • Images: 4 modalities

1. Flip

2. Brightness

3. Rotation

4. Elastic

5. Shift

6. Swirl

1. UNet3D

2. AttUnet3D

3. GAN (Vox2Vox)

4. AttGAN

5. UNet3D with Deformable Convolution

1. Dice Loss

2. Generator Loss

3. Discriminator Loss

• tensorflow
• tensorflow-addons
• tensorflow-cpu
• numpy
• matplotlib
• elasticdeform
• scikit-learn
• scipy
• nibabel
• SimpleITK

Install the requirements using command below:

pip install -r requirements.txt 

Use the following command to see the arguments needed for running:

python -m scripts.main -h

For running UNet3D model use this:

python -m scripts.main -bs 4 -ps 128 -a 5 -ne 1 -ef 0.25 -lr 1e-3 -b1 0.9 -ds 100 -np 2 -aug 1 -m unet

For running AttUnet3D model use this:

python -m scripts.main -bs 4 -ps 128 -a 5 -ne 1 -ef 0.25 -lr 1e-3 -b1 0.9 -ds 100 -np 2 -aug 1 -m att_unet

For running GAN model use this:

python -m scripts.main -bs 4 -ps 128 -a 5 -ne 1 -ef 0.25 -lr 1e-3 -b1 0.9 -ds 100 -np 2 -aug 1 -m gan

For running AttGAN model use this:

python -m scripts.main -bs 4 -ps 128 -a 5 -ne 1 -ef 0.25 -lr 1e-3 -b1 0.9 -ds 100 -np 2 -aug 1 -m att_gan

For running UNet3D_DCN model use this:

python -m scripts.main -bs 4 -ps 16 -a 5 -ne 1 -ef 0.25 -lr 1e-3 -b1 0.9 -ds 100 -np 1 -aug 1 -m unet_dc

For running AttUnet3DDCN model use this:

python -m scripts.main -bs 4 -ps 16 -a 5 -ne 5 -ef 0.25 -lr 1e-3 -b1 0.9 -np 1 -aug 1 -m att_unet_dc

For running AttGANDCN model use this:

python -m scripts.main -bs 1 -ps 16 -a 5 -ne 5 -ef 0.25 -lr 1e-3 -b1 0.9 -np 1 -aug 1 -m att_gan_dc

• Vox2Vox: 3D-GAN for Brain Tumour Segmentation
• Attention Unet
• Deformable Convolution