/MascUnet

Primary LanguagePython

Multi-attention Brain Tumor Image Segmentation Algorithm Based on Unet

Installation

  • Unbuntu 18.04
  • Install TensorFlow 1.14 ,keras 2.1.5 and CUDA 9.0
  • Clone this repo
git clone https://github.com/fdcqqqq/MascUnet
cd MascUnet

Data Preparation

  1. Prepare your dataset under the directory dataset .
  • Directory structure on new dataset needed for training and testing:
    • dataset/train_HGG
    • dataset/train_LGG
    • dataset/test_HGG
    • dataset/test_LGG

Data preprocessing

  • Modify paramter values in data_processing.py
  • Run ./data_processing.py to start data_processing.
python3 data_processing.py

Train

  • Modify paramter values in ./train.py

  • Run ./train.py to start the train the model.

    python3 train.py

Evaluate

  • Specify the model path and test file path in ./predict.py

  • Run ./predict.py to start the evaluation.

    python3 predict.py

  • draw 3D nii predict images to 2D and show figures.

    python3 ./show_slicer_nii.py

Results

  1. Generate images by following specifications under:
  • ./prediction

Figures

Framework

Brats 2019 Dataset

The Brats 2019 contains four modalities:(a): T2. (b): Flair. (c): T1.**(d):**T1c ,and label is (e)Grouth truth,the label include four type of tags,as shown in(e),which are normal tissue(tag 0), necrosis and non-enhancing tumor(tag1),edema(tag 2),and enhancing tumor(tag 4).

Parallel Dilation Convolution Feature Extraction Module

Masc Attention Module

Experiences

Note