HypAST - Hypothalamus Automatic Segmentation Tool

Although magnetic resonance imaging (MRI) is the standard exam to evaluate this region, hypothalamic morphological landmarks are unclear, leading to subjectivity and high variability during manual segmentation. HypAST is a state-of-the-art tool developed to aid researchers on hypothalamus segmentation.

To facilitate the use of our method, we provide here a pip package and a graphical user interface.

Please, check for both bellow.

PIP PACKAGE

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

HypAST requires Python 3.7.

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

GRAPHICAL USER INTERFACE

09/27/22: New version!

03/17/22: Texture analysis updated!

If you wish, you may use our HypAST graphical user interface for prediction, available for Linux.

DOWNLOAD

You can download HypAST-GUI HERE

After downloading, unzip the file and type on the terminal (inside <your_path>/HypAST_0.2/):

        cd dist/HypAST/
        ./HypAST       

After a few seconds, a window will pop-up and you are ready to go!

GETTING STARTED

HypAST-GUI has, at first, three main buttons: "Open file", "Run Code," and "Save Segmentation".

• Click the "Open File" button to choose your .nii or .nii.gz files. A label will appear showing how many files were opened.
• Click the "Run" button. This will generate the segmentations. Using a CPU (i5 8th gen), each volume will take about 6.5s to be done(the first segmentation may take a little longer).

After running, a new menu will appear with new options:

• Volume Information: This window shows a spreadsheet with the volume (in cm3) of all hypothalamus segmented. Besides, it has the dispersion graph and boxplot, where you will analyze possible outliers.
• Texture Information: This window shows a spreadsheet with texture information of all hypothalamus segmented.
• Visualization Tool: This window shows all T1 images and masks generated.
• Save Files: Save your segmentation and attribute files using the "Save Files" button. At this step, you will be saving one .nii mask containing the segmentation for each T1 image opened and two .csv files, one with volume information and the other with texture information. The segmentation files will receive the same name from the original image plus a "_seg" suffix.
• Run another analysis: This button resets the menu, making it possible to run a different analysis.

COMPETITION

We now have a competition running on CodaLab!

DATASET

Our dataset is composed by a total of 1381 hypothalamus masks (manual and "silver standard") from four different datasets: IXI, CC359, OASIS, and MiLI, being the last one a new dataset, created for this benchmark

You can download our dataset HERE

CITATION

If you use MiLI dataset or publish papers based on our method or competiton, please cite us:

@article{rodrigues2022benchmark,

title={A Benchmark for Hypothalamus segmentation on T1-weighted MR Images},

author={Rodrigues, Livia and Rezende, Thiago and Wertheimer, Guilherme and Santos, Yves and Franca, Marcondes and Rittner, Leticia},

journal={NeuroImage},

pages={119741},

year={2022},

publisher={Elsevier}

}

CONTACT

For more information or suggestions, please contact [email protected]