This is the documentation for the FeTS Platform, developed by CBICA at UPenn, in collaboration with Intel Labs, Intel AI and Intel IOT
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Note the ${fets_root_dir}
from Setup.
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
${fets_root_dir}/bin/FeTS_CLI_Segment -d /path/to/output/DataForFeTS \ # data directory after invoking ${fets_root_dir}/bin/PrepareDataset
-a deepMedic,nnunet,deepscan,fets_singlet,fets_triplet \ # all pre-trained models currently available in FeTS see notes below for more details
-lF STAPLE,ITKVoting,SIMPLE,MajorityVoting \ # if a single architecture is used, this parameter is ignored
-g 1 \ # '0': cpu, '1': request gpu
-t 0 # '0': inference mode, '1': training mode
The aforementioned command will perform the following steps:
cd ${fets_root_dir}/data/fets
wget https://upenn.box.com/shared/static/f7zt19d08c545qt3tcaeg7b37z6qafum.zip -O nnunet.zip
unzip nnunet.zip
${fets_root_dir}/data/fets
. The directory structure should look like this:
${fets_root_dir}
│
└───data
│ │
│ └──fets
│ │ │
│ │ └───nnunet
│ │ │ │
│ │ │ └───${training_strategy}.1_bs5 # the specific training strategy
│ │ │ │ │
│ │ │ │ └───fold_${k} # different folds
│ │ │ │ │ │ ...
FeTS_CLI_Segment
applications under the -a
parameter.DataForFeTS
│
└───Patient_001 # this is constructed from the ${PatientID} header of CSV
│ │ Patient_001_brain_t1.nii.gz
│ │ Patient_001_brain_t1ce.nii.gz
│ │ Patient_001_brain_t2.nii.gz
│ │ Patient_001_brain_flair.nii.gz
│ │
│ └──SegmentationsForQC
│ │ │ Patient_001_deepmedic_seg.nii.gz # individual architecture results
│ │ │ Patient_001_nnunet_seg.nii.gz
│ │ │ Patient_001_deepscan_seg.nii.gz
│ │ │ Patient_001_fused_staple_seg.nii.gz # label fusions using different methods
│ │ │ Patient_001_fused_simple_seg.nii.gz
│ │ │ Patient_001_fused_itkvoting_seg.nii.gz
│
└───Pat_JohnDoe
│ │ ...
${fets_root_dir}/bin/FeTS_CLI_Segment -d /path/to/output/DataForFeTS \ # data directory after invoking ${fets_root_dir}/bin/PrepareDataset
-a fets_singlet,fets_triplet \ # can be used with all pre-trained models currently available in FeTS
-lF STAPLE,ITKVoting,SIMPLE,MajorityVoting \ # if a single architecture is used, this parameter is ignored
-g 1 \ # '0': cpu, '1': request gpu
-t 0 # '0': inference mode, '1': training mode
The aforementioned command will run inference using the FeTS Consensus models (both singlet and triplet) for the data directory specified by -d
. The output will be placed in the directory specified by -o
.
Use the FeTS graphical interface (or your preferred GUI annotation tool such as ITK-SNAP or 3D-Slicer) to load each subject’s images:
And each segmentation (either individual architectures or the fusions) separately:
Perform quality control and appropriate manual corrections for each tumor region using the annotation tools:
Label | Region | Acronym |
---|---|---|
1 | Necrotic Core of Tumor | NET |
2 | Peritumoral Edematous & Infiltrated Tissue | ED |
4 | Enhancing/Active part of tumor | ET |
Save the final tumor segmentation as ${SubjectID}_final_seg.nii.gz
under the subject’s directory:
DataForFeTS
│
└───Patient_001 # this is constructed from the ${PatientID} header of CSV
│ │ Patient_001_brain_t1.nii.gz
│ │ Patient_001_brain_t1ce.nii.gz
│ │ Patient_001_brain_t2.nii.gz
│ │ Patient_001_brain_flair.nii.gz
│ │ Patient_001_final_seg.nii.gz # NOTE: training will not work if this is absent!!!
│
└───Pat_JohnDoe
│ │ ...
Before starting final training, please run the following command to ensure the input dataset is as expected:
cd ${fets_root_dir}/bin
./OpenFederatedLearning/venv/bin/python ./SanityCheck.py \
-inputDir /path/to/output/DataForFeTS \
-outputFile /path/to/output/sanity_output.csv
Note: If you are running FeTS version 0.0.2 (you can check version using FeTS_CLI --version
), please do the following to get the SanityChecker for your installation:
cd ${fets_root_dir}/bin
wget https://raw.githubusercontent.com/FETS-AI/Front-End/master/src/applications/SanityCheck.py
During discussions with some of the collaborating sites, we note negative values randomly coming up in the pre-processed scans. To identify these cases, we have now put together an additional script to assess all pre-processed images for the negative values and provide relevant statistics. This can be run in the following manner:
cd ${fets_root_dir}/bin
wget https://raw.githubusercontent.com/FETS-AI/Front-End/master/src/applications/Phase2_IntensityCheck.py
./OpenFederatedLearning/venv/bin/python ./Phase2_IntensityCheck.py \
-inputDir /path/to/output/DataForFeTS \
-outputFile /path/to/output/intensity_check.csv
If /path/to/output/intensity_check.csv
doesn’t get generated, that means the dataset is completely fine, otherwise, please send the file to admin@fets.ai for debugging.
Proceed to training once sanity check (↑) is successfully finished.
If you have a signed certificate from a previous installation, ensure they are copied before trying to train:
cd ${fets_root_dir}/bin/
cp -r ${fets_root_dir_old}/bin/OpenFederatedLearning/bin/federations/pki/client ./OpenFederatedLearning/bin/federations/pki
${fets_root_dir}/bin/FeTS_CLI -d /path/to/output/DataForFeTS \ # input data, ensure "final_seg" is present for each subject
-c ${collaborator_common_name} \ # common collaborator name created during setup
-g 1 -t 1 # request gpu and enable training mode
The aforementioned command will perform the following steps:
${SubjectID}_final_seg.nii.gz
and all 4 structural modalities present) in a collaborative manner