IPLC

👉 Requirements

Non-exhaustive list:

• python3.9+
• Pytorch 1.10.1
• nibabel
• Scipy
• NumPy
• Scikit-image
• yaml
• tqdm
• pandas
• scikit-image
• SimpleITK

👉 Usage

1. Download the M&MS Dataset, and organize the dataset directory structure as follows:

your/data_root/
       train/
            img/
                A/
                    A0S9V9_0.nii.gz
                    ...
                B/
                C/
                ...
            lab/
                A/
                    A0S9V9_0_gt.nii.gz
                    ...
                B/
                C/
                ...
       valid/
            img/
            lab/
       test/
           img/
           lab/

The network takes nii files as an input. The gt folder contains gray-scale images of the ground-truth, where the gray-scale level is the number of the class (0,1,...K).

2. Download the SAM-Med2D model and move the model to the "your_root/pretrain_model" directory in your project.

3. Train the source model in the source domain, for instance, you can train the source model using domain A on the M&MS dataset:

python train_source.py --config "./config/train2d_source.cfg"

4. Adapt the source model to the target domain, for instance, you can adapt the source model to domain B on the M&MS dataset:

python adapt_mian.py --config "./config/adapt.cfg"

🤝 Acknowledgement

• Thanks to the open-source of the following projects: Segment Anything; SAM-Med2D