AEPformer

AEPformer: Asynchronous Enhancement Pattern guided disentangled Transformer for pituitary gland and pituitary microadenoma segmentation in DCE-MRI

Requirements

• python==3.9
• batchgenerators==0.25
• monai==1.3.1
• numpy
• scikit-learn==1.5.0
• scipy==1.13.1
• SimpleITK==2.3.1
• tensorboard==2.17.0
• torch==1.12.1
• torchvision==0.13.1
• tqdm==4.66.4

You can install these packages by executing the following command:

pip install -r requirements.txt

Dataset

This work uses a private dataset. Due to some factors, only part of the data is given here to test the operation of the code in dataset/sample_data.

Training

• Step 1. In the main_train.py file, modify the statement os.environ['CUDA_VISIBLE_DEVICES'] to select the GPU you want to use. For example, set os.environ['CUDA_VISIBLE_DEVICES']='0' to use the first GPU.
• Step 2. In the config.py file, modify the key named dataset_path to specify the data path
• Step 3. Set the training parameters in the config.py file
• Step 4. Execute the command to perform training

python main_train.py

Inference

• Step 1. The breakpoints of the model training will be saved in the runs directory. Select the absolute path of the model breakpoint to be inferred and copy it to the checkpoint_path field in the config.py file.
• Step 2. In the main_test.py file, modify the statement os.environ['CUDA_VISIBLE_DEVICES'] to select the GPU you want to use.
• Step 3. Execute the command to perform inference

python main_test.py