3D-Unet

Chainer implementation of 3D Unet for brain segmentaion.
Training configs are written at configs/base.yml.
Because of the limitation of GPU memory, we used patch based method.

Requirements

• SimpleITK
• Chainer v4
• yaml

Network architecture

3D Unet architecture

Usage

Training
To train 3D unet.

python train.py -h

optional arguments:
  -h, --help            show this help message and exit
  --gpu GPU, -g GPU     GPU ID (negative value indicates CPU)
  --base BASE, -B BASE  base directory path of program files
  --config_path CONFIG_PATH
                        path to config file
  --out OUT, -o OUT     Directory to output the result
  --model MODEL, -m MODEL
                        Load model data
  --resume RESUME, -res RESUME
                        Resume the training from snapshot
  --root ROOT, -R ROOT  Root directory path of input image
  --training_list TRAINING_LIST
                        Path to training image list file
  --validation_list VALIDATION_LIST
                        Path to validation image list file

Example:
To train 3D Unet using gpu

python train.py -g 0

Prediction
To predict images with trained network.

python predict.py -h

optional arguments:
  -h, --help            show this help message and exit
  --gpu GPU, -g GPU     GPU ID (negative value indicates CPU)
  --base BASE, -B BASE  base directory path of program files
  --config_path CONFIG_PATH
                        path to config file
  --out OUT, -o OUT     Directory to output the result
  --model MODEL, -m MODEL
                        Load model data(snapshot)
  --root ROOT, -R ROOT  Root directory path of input image
  --test_list TEST_LIST
                        Path to test image list file

Example:
To predict label using gpu

python predict.py -g 0 -m results/training/UNet3D_150000.npz

Training result

Training loss and dice score.

Predicted result

Example of input image

Example of ground truth

Example of prediction

Results

We calculated jaccard index of image shown above.