This project uses U-Net Architecture to create segmentation masks for brain tumor images.

• Dataset Used
• Data Augmentation
• Model Architecture
• Training Process

Dataset used in this project was provided by Jun Cheng.
This dataset contains 3064 T1-weighted contrast-enhanced images with three kinds of brain tumor. For a detailed information about the dataset please refer to this site. Version 5 of this dataset is used in this project. Each image is of dimension 512 x 512 x 1 , these are black and white images thus having a single channel.
Some Data Samples

Original Image	Mask Image

The basic forms of data augmentation are used here to diversify the training data. All the augmentation methods are used from Pytorch's Torchvision module.

• Horizontally Flip
• Vertically Flip
• Rotation Between 75°-15°

Code Responsible for augmentation

Each augmentation method has a probability of 0.5 and the order of application is also random. For Rotation Augmentation the degree of rotation is chosen randomly between 75°-15°.

The model architecture is depicted in this picture.

The model was trained on a Nvidia GTX 1050Ti 4GB GPU. Total time taken for model training was 6 hours and 45 minutes. We started with an initial learning rate of 1e-3 and reduced it by 85% on plateauing, final learning rate at the end of 100 epochs was 2.7249e-4.
Some graphs indicating Learning Rate & Loss Value over 100 epochs are given below.

Learning Rate Graph in Tensorboard.

Loss Graph Plotted in Matplotlib
Loss Graph Plotted in Tensorboard

To see the complete output produced during the training process check this

This project uses python3.

Clone the project.

git clone https://github.com/Jeetu95/Brain-Tumor-Segmentation.git

Install Pytorch from this link
Use pip to install all the dependencies

pip install -r requirements.txt

To open the notebook

jupyter lab

To see logs in Tensorboard

tensorboard --logdir logs --samples_per_plugin images=100

To setup the project dataset

python setup_scripts/download_dataset.py
python setup_scripts/unzip_dataset.py
python setup_scripts/extract_images.py

python api.py --file <file_name> --ofp <optional_output_file_path>
python api.py --folder <folder_name> --odp <optional_output_folder_path>
python api.py -h

Available api.py Flags
--file  : A single image file name.
--ofp   : An optional folder location only where the output image will be stored. Used only with --file tag.

--folder: Path of a folder containing many image files.
--odp   : An optional folder location only where the output images will be stored. Used only with --folder tag.

-h, --help : Shows the help contents.

Some results generated by API are here

The mean Dice Score our model gained was 0.74461 in testing dataset of 600 images.
From this we can conclude that in our testing dataset our constructed mask has a similarity of about 74% with the original mask.
Some samples from our training dataset output are below. The top best results are here.To see all the results click on this Google Drive link

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Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

MIT License