FeTS Platform

This is the documentation for the FeTS Platform, developed by CBICA at UPenn, in collaboration with Intel Labs, Intel AI and Intel IOT

Application Setup
Process the Data
Run the Application
ITCR Connectivity

Processing the Data for FeTS

Note the ${fets_root_dir} from Setup.

Table of Contents

Application Path

cd ${download_location}
${fets_root_dir}/bin/FeTS # launches application

Please add the following path to your LD_LIBRARY_PATH when using FeTS: ${fets_root_dir}/lib:

export LD_LIBRARY_PATH=${fets_root_dir}/lib:$LD_LIBRARY_PATH

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Data Arrangement

Note the ${fets_root_dir} from Setup.

For the first application of FeTS in volumetric brain tumor MRI scans, you should follow the pre-processing pipeline defined in the International Brain Tumor Segmentation (BraTS) Challenge:


NOTE: For some OS variants, we have seen PrepareDataset executable to cause issues, for which we have an alternative with ${fets_root_dir}/bin/PrepareDataset.py, which has the exact same API and can be invoked in the following way:

cd ${fets_root_dir}/bin
./OpenFederatedLearning/venv/bin/python \ # virtual environment that was set up in previous section
  ./PrepareDataset.py -i /path/to/raw_data.csv -o /path/to/output

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Starting from Pre-processed data

NOTE: Skip this step if you have used PrepareDataset as described in https://fets-ai.github.io/Front-End/process_data#pre-processing

If you have processed data from a prior study that you would like to include in the FeTS federation, please ensure that all the data is co-registered within each patient and the annotations are in the same space. Once that is assured, follow these steps:

  1. Arrange your data by using the processed image files in respective columns
  2. Run PrepareDataset as shown above
  3. In DataForQC, under each patient, the transformation matrices will be generated per modality. Use the T1CE_to_SRI.mat file (the assumption here is that the data is co-registered within each patient) to transform the annotation (which is in the patient space) in the following manner:
    ${fets_root_dir}/bin/Preprocessing \
      -i /path/to/patient_X/annotation.nii.gz \
      -rFI ${fets_root_dir}/data/sri24/atlasImage.nii.gz \
      -o /path/to/output/DataForFeTS/patient_X/annotation_final_seg.nii.gz \
      -rIA /path/to/output/DataForQC/patient_X/T1CE_to_SRI.mat \
      -reg Rigid -rIS 1 -rSg 1
  4. Load the transformed images and corresponding annotation to ensure they are aligned correctly:
    • /path/to/output/DataForFeTS/patient_X/brain_*
    • /path/to/output/DataForFeTS/patient_X/annotation_final_seg.nii.gz

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Next: Run Application