Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020)

Our code is origin from UA-MT

You can find paper in Arxiv.

1. Clone the repo:

git clone https://github.com/kleinzcy/SASSnet.git 
cd SASSnet

1. Put the data in data/2018LA_Seg_Training Set.

2. Train the model

cd code
# for 16 label
python train_gan_sdfloss.py --gpu 0 --label 16 --consistency 0.01 --exp model_name
# for 8 label
python train_gan_sdfloss.py --gpu 0 --label 8 --consistency 0.015 --exp model_name

Params are the best setting in our experiment.

1. Test the model

python test_LA.py --model model_name --gpu 0 --iter 6000

Our best model are saved in model dir.

If you find our work is useful for you, please cite us.