hypast 0.0.5

Hypothalamus Automatic Segmentation Tool

HypAST - Hypothalamus Automatic Segmentation Tool

On this package you will find a trained model for hypothalamus segmentation on T1 MRI images and a trainable class, in case you wish to use your own data.

This tool is not suitable for clinical purposes.

INSTALLATION

    pip install hypast==0.0.3

HypAST requires Python 3.7 and PyTorch 1.9

To install PyTorch:

    # CUDA 10.2
    conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
    # CUDA 11.3
    install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=11.3 -c pytorch -c conda-forge
    # CPU Only
    conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cpuonly -c pytorch

GETTING STARTED

HypAST works on .nii or .nii.gz input files for images and .nii, .nii.gz or .npy for annotations. Using HypAST you will be able to predict hypothalamus segmentation using our model or to train with your own data.

See more bellow:

Trainer

With Trainer you can train the model using your own data.

Example:

    import hypast as hyp
    train = hyp.Trainer(train_path, chkp_path, val_path, maxep=200, accum=16, weight=[1,4], lr=5e-3, bs=8)
    train.trainer()

• Input:

  • train_path: path to h5py train set
  • chkp_path: checkpoint path
  • val_path: path to h5py val set
  • maxep: Maximum # of epochs in training (defaul = 200)
  • accum: Batch accumulation (defaul = 16)
  • weight: Cross Entropy Weight (defaul = [1,4])
  • lr: Learning Rate (defaul = 5e-3)
  • bs: Batch Size (defaul = 8)

• Output:

  • Checkpoint file on defined path

CreateHDF5

To facilitate training using HypAST, CreateHDF5 will adjust your data for you.

Example:

    import hypast as hyp
    hyp.CreateHDF5(list_data, list_labels, out_path)
    create.create_links() 

• Input:

  • list_data: List containing paths of .nii or .nii.gz images
  • list_labels: List containing paths of labels (.nii, .nii.gz or .npy)
  • out_path: Path were .hdf5 files will be saved

• Output:

  • Return train.hdf5 and val.hdf5 on defined path

Predictor

With HypAST you can also generate hypothalamus segmentation using our trained model.

Example:

    import hypast as hyp
    pred = hyp.Predictor(list_data, out)
    pred.predictor()

• Input:

  • list_data: List containing paths of .nii or .nii.gz files to be segmented
  • out: Path were segmentation will be saved

• Output:

  • Segmentation files on defined path

CONTACT

For more information or suggestions, please contact [e-mail]

See more on https://github.com/MICLab-Unicamp/HypAST