This repo refactors and extends the repo of 4Dsegment2.0
CMRSegment is a Cardiac Magnetic Resonance Imaging processing pipeline, consisted of 6 modules, which are in sequence a preprocessor, a segmentor, a refiner, an extractor, a coregister, and a motion tracker.
To install CMRSegment, do
pip setup.py install
It depends on MIRTK. After you have installed MIRTK, you need to add mirtk to pythonpath
export PYTHONPATH=$PYTHONPATH:CMAKE_INSTALL_PREFIX/Lib/Python
To add mirtk to your virtual environment
Uses add2virtualenv from virtualenvwrapper: https://virtualenvwrapper.readthedocs.io/en/latest/command_ref.html#add2virtualenv
pip install virutalenvwrapper (virtualenvwrapper-win for windows)
add2virtualenv CMAKE_INSTALL_PREFIX/Lib/Python
Then you can directly import mirtk in your python script.
A prebuild docker image is available to use:
docker pull lisurui6/cmr-sgement:latest
docker run -ti lisurui6/cmr-segment:latest
export PYTHONPATH=$PYTHONPATH:/usr/local/lib/mirtk/python
docker build -t lisurui6/cmr-segment:latest .
docker push docker.io/lisurui6/cmr-segment:latest
eval $(ssh-agent -s)
ssh-add ~/.ssh/id_rsa
export PYTHONPATH=$PYTHONPATH:/mirtk/lib/python
rf -rf PH_atlas
apt install python3.7
pip2 uninstall tensorflow
python3.7 get-pip.py
pip3.7 install -r requirements.txt
pip3.7 install tensorflow
pip3.7 install torch torchvision
This module splits the CMR sequence, and then resamples and enlarges each phase. It assumes that input subjects are in the following file structure:
/path/
subject1/
LVSA.nii.gz
subject2/
LVSA.nii.gz
...
It then generates output in the following structures:
/path/
subject1/
contrasted_LVSA.nii.gz -> Contrasted CMR sequence image
lvsa_ED.nii.gz -> ED phase image
lvsa_ES.nii.gz -> ES phase image
lvsa_SR_ED.nii.gz -> Enlarged ED image
lvsa_SR_ES.nii.gz -> Enlarged ES image
lvsa_SR_ED_resampled.nii.gz -> Resampled ED image
lvsa_SR_ESs_resampled.nii.gz -> Resampled ES image
gray_phases/
lvsa_00.nii.gz -> Phase 0 image
lvsa_01.nii.gz -> Phase 1 image
...
resampled/
lvsa_0.nii.gz -> Resampled phase 0 image
lvsa_1.nii.gz -> Resampled phase 1 image
...
enlarged/
lvsa_0.nii.gz -> Enlarged phase 0 image
lvsa_1.nii.gz -> Enlarged phase 1 image
...
subject2/
...
To run only the preprocessor, use one of the following commands:
cmrtk-pipeline -o /output-dir/ --data-dir /input-dir/
python -m CMRSegment.preprocessor -o /output-dir/ --data-dir /input-dir
cmrtk-preprocessor -o /output-dir/ --data-dir /input-dir/
This module uses a trained 3D convolutional neural network (UNet) to segment ED/ES enlarged images.
The code to train the network is in experiments/fcn_3d/
It takes input
/path/
subject1/
lvsa_SR_ED.nii.gz -> Enlarged ED image
lvsa_SR_ES.nii.gz -> Enlarged ES image
enlarged/
lvsa_0.nii.gz -> Enlarged phase 0 image
lvsa_1.nii.gz -> Enlarged phase 1 image
...
subject2/
...
and generates output
/path/
subject1/
seg_lvsa_SR_ED.nii.gz -> ED segmentation
seg_lvsa_SR_ES.nii.gz -> ES segmentation
segs/
lvsa_0.nii.gz -> Phase 0 segmentation
lvsa_1.nii.gz -> Phase 1 segmentation
...
4D_rview/
4Dimg.nii.gz -> 4D image
4Dseg.nii.gz -> 4D segmentation
subject2/
...
To run only the segmentor, use one of the following commands:
cmrtk-pipeline -o /output-dir/ --data-dir /input-dir/ --segment --model-path /path-to-model --torch --segment-cine
python -m CMRSegment.segmentor -o /output-dir/ --data-dir /input-dir --segment --model-path /path-to-model --torch --segment-cine
cmrtk-torch-segmentor -o /output-dir/ --data-dir /input-dir/ --segment --model-path /path-to-model --torch --segment-cine
For RBH project, a pretrained model is located at /cardiac/DL_segmentation/IHD_project/DL_models/inference_model.pt
Refiner uses registration to refine segmentations generated by the segmentor. For each segmentation, it first finds top similar atlases, then registers the segmentation with each atlas, and finally fuses the labels of all transformed atlases and outputs as the refined segmentation.
It takes input from
/path/
subject1/
seg_lvsa_SR_ED.nii.gz -> ED segmentation
seg_lvsa_SR_ES.nii.gz -> ES segmentation
subject2/
...
and generates output
/path/
subject1/
refine/
tmp/ -> Temporary files
seg_lvsa_SR_ED.nii_refined.nii.gz -> Refined ED segmentation
seg_lvsa_SR_ES.nii_refined.nii.gz -> Refined ES segmentation
subject2/
...
To run only the refiner, use one of the following commands:
cmrtk-pipeline -o /output-dir/ --data-dir /input-dir/ --refine --csv-path /path-to-csv --n-top 7 --n-atlas 500
python -m CMRSegment.refiner -o /output-dir/ --data-dir /input-dir --refine --csv-path /path-to-csv --n-top 7 --n-atlas 500
cmrtk-refiner -o /output-dir/ --data-dir /input-dir/ --refine --csv-path /path-to-csv --n-top 7 --n-atlas 500
--csv-path indicates a csv file that contains a list of atlas meshes to use. For RBH project, the csv file is at
/cardiact/RBH_3D_atlases/3D.csv
Extractor extracts 3D triangular mesh from 3D segmentations using marching cubes algorithms. It also extracts landmarks.
It takes input from
/path/
subject1/
seg_lvsa_SR_ED.nii.gz -> ED segmentation
seg_lvsa_SR_ES.nii.gz -> ES segmentation
subject2/
...
or from
/path/
subject1/
refine/
seg_lvsa_SR_ED.nii_refined.nii.gz -> Refined ED segmentation
seg_lvsa_SR_ES.nii_refined.nii.gz -> Refined ES segmentation
subject2/
...
and generates output
/path/
subject1/
landmark_ED.vtk -> ED landmarks
landmark_ES.vtk -> ES landmarks
mesh/
LVendo_ED.vtk -> LV Endo ED mesh
LVendo_ES.vtk -> LV Endo ES mesh
LVepi_ED.vtk -> LV Epi ED mesh
LVepi_ES.vtk -> LV Epi ES mesh
LVmyo_ED.vtk -> LV Myo ED mesh
LVmyo_ES.vtk -> LV Myo ES mesh
RV_ED.vtk -> RV ED mesh
RV_ES.vtk -> RV ES mesh
RVepi_ED.vtk -> RV Epi ED mesh
RVepi_ES.vtk -> RV Epi ES mesh
subject2/
...
To run only the extractor, use one of the following commands:
cmrtk-pipeline -o /output-dir/ --data-dir /input-dir/ --extract
python -m CMRSegment.extractor -o /output-dir/ --data-dir /input-dir --extract
cmrtk-extractor -o /output-dir/ --data-dir /input-dir/ --extract
Coregister registers each subject mesh with an atlas and transform each subject to the same atlas space.
It takes input from
/path/
subject1/
landmark_ED.vtk -> ED landmarks
landmark_ES.vtk -> ES landmarks
mesh/
LVendo_ED.vtk -> LV Endo ED mesh
LVendo_ES.vtk -> LV Endo ES mesh
LVepi_ED.vtk -> LV Epi ED mesh
LVepi_ES.vtk -> LV Epi ES mesh
LVmyo_ED.vtk -> LV Myo ED mesh
LVmyo_ES.vtk -> LV Myo ES mesh
RV_ED.vtk -> RV ED mesh
RV_ES.vtk -> RV ES mesh
RVepi_ED.vtk -> RV Epi ED mesh
RVepi_ES.vtk -> RV Epi ES mesh
subject2/
...
and generates output
/path/
subject1/
registration/
temp/ -> Temporary files
debug/ -> Intermediate meshes
landmarks.dof.gz
rigid/ -> Meshes after rigid transformation
LVendo_ED.vtk -> LV Endo ED mesh
LVendo_ES.vtk -> LV Endo ES mesh
LVepi_ED.vtk -> LV Epi ED mesh
LVepi_ES.vtk -> LV Epi ES mesh
LVmyo_ED.vtk -> LV Myo ED mesh
LVmyo_ES.vtk -> LV Myo ES mesh
RV_ED.vtk -> RV ED mesh
RV_ES.vtk -> RV ES mesh
RVepi_ED.vtk -> RV Epi ED mesh
RVepi_ES.vtk -> RV Epi ES mesh
nonrigid/ -> Meshes after nonrigid transformation
LVendo_ED.vtk -> LV Endo ED mesh
LVendo_ES.vtk -> LV Endo ES mesh
LVepi_ED.vtk -> LV Epi ED mesh
LVepi_ES.vtk s -> LV Epi ES mesh
LVmyo_ED.vtk -> LV Myo ED mesh
LVmyo_ES.vtk -> LV Myo ES mesh
RV_ED.vtk -> RV ED mesh
RV_ES.vtk -> RV ES mesh
subject2/
...
To run only the coregister, use one of the following commands:
cmrtk-pipeline -o /output-dir/ --data-dir /input-dir/ --coregister --template-dir /template-dir --param-dir /param-dir
python -m CMRSegment.coregister -o /output-dir/ --data-dir /input-dir --coregister --template-dir /template-dir --param-dir /param-dir
cmrtk-coregister -o /output-dir/ --data-dir /input-dir/ --coregister --template-dir /template-dir --param-dir /param-dir
Default template dir is input/params
Default param dir is lib/CMRSegment/resource
Pipeline links all the above modules together.
cmrtk-pipeline -o /output-dir/ --data-dir /input-dir/
(if you want to run segmentor)
--segment
--model-path /path
--segment-cine
--torch
(if you want to run refiner)
--refine
--csv-path /path
--n-top 7
--n-atlas 500
(if you want to run extractor)
--extract
--iso-value 120
--blur 2
(if you want to run coregister)
--coregister
--template-dir /path
--param-dir /path
To see details of these options, look at argument_parser() function in the file pipeline/config.py