Stanford Fullysampled 3D FSE Knees 2019 Dataset

This project folder contains the configuration files, preprocessing, and visualization scripts for the Stanford Fullysampled 3D FSE Knees 2019 dataset.

For more information, please refer to http://mridata.org/list?project=Stanford%20Fullysampled%203D%20FSE%20Knees.

Note

When running the preprocessing scripts please make sure you have the ismrmrd package installed. You can install it with the following command:

pip install -r requirements/requirements-ahead_stanfordknees.txt

Visualization An example notebook for visualizing the data is provided in the visualize notebook. You just need to set the path where the dataset is downloaded.

Preprocessing The preprocessing pipeline is implemented in the preprocess_dataset.sh script, consisting of the following steps: 1. Convert the data from ISMRMRD to HDF5 format. 2. Split the dataset into training and validation sets.

Training/Testing

Important

The Stanford Knees dataset is natively supported in atommic. Therefore, you do not need to create a custom dataset class. You just need to set the dataset_format argument in the configuration file to the desired Stanford Knees dataset version. Also the FFT needs to be centered. For example:

model:
    fft_centered: true
    fft_normalization: ortho

train_ds:
    dataset_format: stanford_knees
    fft_centered: true
    fft_normalization: ortho

validation_ds:
    dataset_format: stanford_knees
    fft_centered: true
    fft_normalization: ortho

test_ds:
    dataset_format: stanford_knees
    fft_centered: true
    fft_normalization: ortho

For training a model, you just need to set up the data and export paths to the configuration file of the model you want to train. In train_ds and validation_ds please set the data_path to the generated json files. In exp_manager please set the exp_dir to the path where you want to save the model checkpoints and tensorboard or wandb logs.

You can train a model with the following command:

atommic run -c /projects/REC/StanfordKnees2019/conf/train/{model}.yaml

For testing a model, you just need to set up the data and export paths to the configuration file model you want to test. In checkpoint (line 2) set the path the trained model checkpoint and in test_ds please set the data_path. In exp_manager please set the exp_dir to the path where the predictions and logs will be saved.

You can test a model with the following command:

atommic run -c /projects/REC/StanfordKnees2019/conf/test/{model}.yaml

Note: The default logger is tensorboard.