Volumetric segmentation using a V-Net model
We recommend on using the following docker image. Pull the following docker image using:
docker pull geromev/dsgzneuro:latest
Alternatively, you can also:
pip install -r requirements.txt
All volumes should be in ./vols_all/
.
We use Segmentation3DTrainer
object in trainer.py
which performs data loading,
data augmentation, and training. As an example usage to train a model you can
do the following:
python trainer_example_usage.py --train_model=1
Training will ouptut the following:
- Save weights of the trained model at
./model/
. - Output tensorboard event logs at
./logs/
. - If
debug_dump_image
is set to True, will save augmented volumes at./augmented_vols
.
When training is finished, the trained model will be used to evaluate the test set.. You can evaluate the test set using the following:
python trainer_example_usage.py --train_model=0
Predicted segmentation outputs will be in ./pred_output/
.
This project is licensed under the MIT License - see the LICENSE.md file for details