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Remove unused code
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@valosekj
valosekj committed Jan 20, 2023
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277 changes: 2 additions & 275 deletions scripts-t2w_csa/sct-preprocess_data.sh
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Original file line number Diff line number Diff line change
@@ -1,11 +1,10 @@
#!/bin/bash
#
# Preprocess data.
# Compute cross-sectional area (CSA) at C2-C3 vertebral levels from T2w images for CanProCo dataset
#
# Dependencies (versions):
# - SCT (5.x.x)
# - SCT (5.8)
#
# reference from https://github.com/spine-generic/spine-generic/blob/3aa69465063d823088b2c819d3ad9b81725b2fc8/process_data.sh#L246

# Usage:
# sct_run_batch -c <PATH_TO_REPO>/etc/config_preprocess_data.json
Expand Down Expand Up @@ -49,38 +48,6 @@ echo "PATH_QC: ${PATH_QC}"
# CONVENIENCE FUNCTIONS
# ======================================================================================================================

lesionseg_if_does_not_exist() {
###
# This function checks if a manual lesions segmentation file already exists, then:
# - If it does, copy it locally.
# - If it doesn't, perform automatic lesions segmentation
# This allows you to add manual segmentations on a subject-by-subject basis without disrupting the pipeline.
###
local file="$1"
local contrast="$2"
local centerline="$3"
# Update global variable with segmentation file name
FILELESION="${file}_lesionseg"
FILELESIONMANUAL="${PATH_DATA}/derivatives/labels/${SUBJECT}/anat/${FILELESION}-manual.nii.gz"
echo
echo "Looking for manual segmentation: $FILELESIONMANUAL"
if [[ -e $FILELESIONMANUAL ]]; then
echo "Found! Using manual segmentation."
rsync -avzh $FILELESIONMANUAL ${FILELESION}.nii.gz
sct_qc -i ${file}.nii.gz -s ${FILELESION}.nii.gz -p sct_deepseg_lesion -qc ${PATH_QC} -qc-subject ${SUBJECT}
else
echo "Not found. Proceeding with automatic segmentation."
# Lesions segmentation
# Note, sct_deepseg_lesion does not have QC implemented yet, see: https://github.com/spinalcordtoolbox/spinalcordtoolbox/issues/3803
if [[ ${centerline} == "" ]];then
sct_deepseg_lesion -i ${file}.nii.gz -c ${contrast} -ofolder without_centerline
else
sct_deepseg_lesion -i ${file}.nii.gz -centerline file -file_centerline ${centerline} -c ${contrast} -ofolder with_centerline
fi

fi
}

segment_if_does_not_exist() {
###
# This function checks if a manual spinal cord segmentation file already exists, then:
Expand Down Expand Up @@ -211,188 +178,8 @@ if [[ -f ${file_t2w}.nii.gz ]];then

# Compute average cord CSA between C2 and C3
sct_process_segmentation -i ${file_t2w}_seg.nii.gz -vert 2:3 -vertfile ${file_t2w}_seg_labeled.nii.gz -o ${PATH_RESULTS}/csa-SC_T2w.csv -append 1
exit

# MS lesions segmentation
# TODO - explore why sct_deepseg_lesion produces different results with manually provided centerline
lesionseg_if_does_not_exist ${file_t2w} 't2'
# Get centerline from deppeseg SC seg
sct_get_centerline -i ${file_t2w}_seg.nii.gz -method fitseg
lesionseg_if_does_not_exist ${file_t2w} 't2' ${file_t2w}_seg_centerline.nii.gz
exit
fi

# -------------------------------------------------------------------------
# PSIR
# -------------------------------------------------------------------------
# Add suffix corresponding to contrast
file_psir=${file}_PSIR
# Check if PSIR image exists
if [[ -f ${file_psir}.nii.gz ]];then
# Make sure the image metadata is a valid JSON object
if [[ ! -s ${file_psir}.json ]]; then
echo "{}" >> ${file_psir}.json
fi

# Rename raw file
mv ${file_psir}.nii.gz ${file_psir}_raw.nii.gz

# Reorient to RPI
sct_image -i ${file_psir}_raw.nii.gz -setorient RPI -o ${file_psir}_raw_RPI.nii.gz

# Create a symbolic link to _raw_RPI file (to be BIDS compliant)
mv ${file_psir}_raw_RPI.nii.gz ${file_psir}.nii.gz

# Spinal cord segmentation
# Note: For PSIR images, we use sct_propseg. Generally, it works better than sct_deepseg_sc.
segment_if_does_not_exist ${file_psir} 't1' 'propseg'
fi

# -------------------------------------------------------------------------
# STIR
# -------------------------------------------------------------------------
# Add suffix corresponding to contrast
file_stir=${file}_STIR
# Check if STIR image exists
if [[ -f ${file_stir}.nii.gz ]];then
# Make sure the image metadata is a valid JSON object
if [[ ! -s ${file_stir}.json ]]; then
echo "{}" >> ${file_stir}.json
fi

# Rename raw file
mv ${file_stir}.nii.gz ${file_stir}_raw.nii.gz

# Reorient to RPI
sct_image -i ${file_stir}_raw.nii.gz -setorient RPI -o ${file_stir}_raw_RPI.nii.gz

# Create a symbolic link to _raw_RPI file (to be BIDS compliant)
mv ${file_stir}_raw_RPI.nii.gz ${file_stir}.nii.gz

# Spinal cord segmentation
# Note: For STIR images, we use sct_propseg. Generally, it works better than sct_deepseg_sc.
segment_if_does_not_exist ${file_stir} 't2' 'propseg'
fi

# ------------------------------------------------------------------------------
# T2star
# ------------------------------------------------------------------------------
# Add suffix corresponding to contrast
file_t2s="${file}_T2star"
# Check if T2star image exists
if [[ -f ${file_t2s}.nii.gz ]];then
# Make sure the image metadata is a valid JSON object
if [[ ! -s ${file_t2s}.json ]]; then
echo "{}" >> ${file_t2s}.json
fi

# Rename raw file
mv ${file_t2s}.nii.gz ${file_t2s}_raw.nii.gz

# Reorient to RPI
sct_image -i ${file_t2s}_raw.nii.gz -setorient RPI -o ${file_t2s}_raw_RPI.nii.gz
# Compute root-mean square across 4th dimension (if it exists)
sct_maths -i ${file_t2s}_raw_RPI.nii.gz -rms t -o ${file_t2s}_raw_RPI_rms.nii.gz

# Create a symbolic link to _raw_RPI_rms file (to be BIDS compliant)
mv ${file_t2s}_raw_RPI_rms.nii.gz ${file_t2s}.nii.gz

# Spinal cord segmentation
# Note: For T2star images, we use sct_deepseg_sc
segment_if_does_not_exist ${file_t2s} 't2s' 'deepseg'

# TODO - bring vertebral levels from T2w into T2star - so far we do not have T2w_seg_labeled since we are working
# on manual T2w SC seg corrections
# sct_register_multimodal -i label_T1w/template/PAM50_levels.nii.gz -d ${file_t2s}.nii.gz -o PAM50_levels2${file_t2s}.nii.gz -identity 1 -x nn

fi
# # ------------------------------------------------------------------------------
# # MTS
# # ------------------------------------------------------------------------------
# # Go to subject folder for source images
# cd ${SUBJECT}/anat
# # Define variables
# # We do a substitution '/' --> '_' in case there is a subfolder 'ses-0X/'
# file="${SUBJECT//[\/]/_}"

# file_t1w="${file}_acq-T1w_MTS"
# file_mton="${file}_acq-MTon_MTS"
# file_mtoff="${file}_acq-MToff_MTS"
# file_mt="${file}_acq-MT_MTS"

# if [[ -e "${file_t1w}.nii.gz" && -e "${file_mton}.nii.gz" && -e "${file_mtoff}.nii.gz" && -e "${file_mt}.nii.gz" ]]; then
# # Segment lesion (only if it does not exist)
# lesionseg_if_does_not_exist $file_t1w "t1" ${CENTERLINE_METHOD}
# file_lesionseg_t1w=$FILELESION

# # Create mask
# sct_create_mask -i ${file_t1w}.nii.gz -p centerline,${file_t1w_seg}.nii.gz -size 35mm -o ${file_t1w}_mask.nii.gz
# # Crop data for faster processing
# sct_crop_image -i ${file_t1w}.nii.gz -m ${file_t1w}_mask.nii.gz -o ${file_t1w}_crop.nii.gz
# # file_t1w="${file_t1w}_crop"

# # Redefine variable for final SC segmentation mask as path changed
# file_seg_dil_t1w=${PATH_DATA_PROCESSED}/${SUBJECT}/anat/${file_t1w_seg}_dilate
# # Make sure the first rater metadata is a valid JSON object
# if [[ ! -s ${file_gt}.json ]]; then
# echo "{}" >> ${file_gt}.json
# fi
# # Crop the manual seg
# sct_crop_image -i ${file_gt}.nii.gz -m ${file_seg_dil_t1w}.nii.gz -o ${file_gt}_crop.nii.gz
# # Go back to the root output path
# cd $PATH_OUTPUT
# # Create and populate clean data processed folder for training
# PATH_DATA_PROCESSED_CLEAN="${PATH_DATA_PROCESSED}_clean"
# # Copy over required BIDs files
# mkdir -p $PATH_DATA_PROCESSED_CLEAN $PATH_DATA_PROCESSED_CLEAN/${SUBJECT} $PATH_DATA_PROCESSED_CLEAN/${SUBJECT}/anat
# rsync -avzh $PATH_DATA_PROCESSED/dataset_description.json $PATH_DATA_PROCESSED_CLEAN/
# rsync -avzh $PATH_DATA_PROCESSED/participants.* $PATH_DATA_PROCESSED_CLEAN/
# rsync -avzh $PATH_DATA_PROCESSED/README $PATH_DATA_PROCESSED_CLEAN/
# rsync -avzh $PATH_DATA_PROCESSED/dataset_description.json $PATH_DATA_PROCESSED_CLEAN/derivatives/
# # For lesion segmentation task, copy SC crops as inputs and lesion annotations as targets
# rsync -avzh $PATH_DATA_PROCESSED/${SUBJECT}/anat/${file}_crop.nii.gz $PATH_DATA_PROCESSED_CLEAN/${SUBJECT}/anat/${file}.nii.gzROCESSED_CLEAN/${SUBJECT}/anat/${file}.json
# mkdir -p $PATH_DATA_PROCESSED_CLEAN/derivatives $PATH_DATA_PROCESSED_CLEAN/derivatives/labels $PATH_DATA_PROCESSED_CLEAN/derivatives/labels/${SUBJECT} $PATH_DATA_PROCESSED_CLEAN/derivatives/labels/${SUBJECT}/anat/
# rsync -avzh $PATH_DATA_PROCESSED/derivatives/labels/${SUBJECT}/anat/${file_gt}_crop.nii.gz $PATH_DATA_PROCESSED_CLEAN/derivatives/labels/${SUBJECT}/anat/${file_gt}.nii.gz
# rsync -avzh $PATH_DATA_PROCESSED/derivatives/labels/${SUBJECT}/anat/${file_gt}.json $PATH_DATA_PROCESSED_CLEAN/derivatives/labels/${SUBJECT}/anat/${file_gt}.json


# # Register PD (mtoff)->T1w
# # Tips: here we only use rigid transformation because both images have very similar sequence parameters. We don't want to use SyN/BSplineSyN to avoid introducing spurious deformations.
# sct_register_multimodal -i ${file_mtoff}.nii.gz -d ${file_t1w}.nii.gz -dseg ${file_t1w_seg}.nii.gz -param step=1,type=im,algo=rigid,slicewise=1,metric=CC -x spline -qc ${PATH_QC} -qc-subject ${SUBJECT}
# file_mtoff="${file_mtoff}_reg"
# # Register MTon->T1w
# sct_register_multimodal -i ${file_mton}.nii.gz -d ${file_t1w}.nii.gz -dseg ${file_t1w_seg}.nii.gz -param step=1,type=im,algo=rigid,slicewise=1,metric=CC -x spline -qc ${PATH_QC} -qc-subject ${SUBJECT}
# file_mton="${file_mton}_reg"
# # Register MT->T1w
# sct_register_multimodal -i ${file_mt}.nii.gz -d ${file_t1w}.nii.gz -dseg ${file_t1w_seg}.nii.gz -param step=1,type=im,algo=rigid,slicewise=1,metric=CC -x spline -qc ${PATH_QC} -qc-subject ${SUBJECT}
# file_mton="${file_mton}_reg"
# # Copy json files to match file basename (it will later be used by sct_compute_mtsat)
# cp ${SUBJECT}_acq-T1w_MTS.json ${file_t1w}.json
# cp ${SUBJECT}_acq-MToff_MTS.json ${file_mtoff}.json
# cp ${SUBJECT}_acq-MTon_MTS.json ${file_mton}.json
# # # Register template->T1w_ax (using template-T1w as initial transformation)
# # sct_register_multimodal -i $SCT_DIR/data/PAM50/template/PAM50_t1.nii.gz -iseg $SCT_DIR/data/PAM50/template/PAM50_cord.nii.gz -d ${file_t1w}.nii.gz -dseg ${file_t1w_seg}.nii.gz -param step=1,type=seg,algo=slicereg,metric=MeanSquares,smooth=2:step=2,type=im,algo=syn,metric=CC,iter=5,gradStep=0.5 -initwarp warp_template2T1w.nii.gz -initwarpinv warp_T1w2template.nii.gz
# # # Rename warping field for clarity
# # mv warp_PAM50_t12${file_t1w}.nii.gz warp_template2axT1w.nii.gz
# # mv warp_${file_t1w}2PAM50_t1.nii.gz warp_axT1w2template.nii.gz
# # # Warp template
# # sct_warp_template -d ${file_t1w}.nii.gz -w warp_template2axT1w.nii.gz -ofolder label_axT1w -qc ${PATH_QC} -qc-subject ${SUBJECT}
# # # Compute MTR
# # sct_compute_mtr -mt0 ${file_mtoff}.nii.gz -mt1 ${file_mton}.nii.gz
# # # Compute MTsat
# # sct_compute_mtsat -mt ${file_mton}.nii.gz -pd ${file_mtoff}.nii.gz -t1 ${file_t1w}.nii.gz
# # # Extract MTR, MTsat and T1 in WM between C2 and C5 vertebral levels
# # sct_extract_metric -i mtr.nii.gz -f label_axT1w/atlas -l 51 -vert 2:5 -vertfile label_axT1w/template/PAM50_levels.nii.gz -o ${PATH_RESULTS}/MTR.csv -append 1
# # sct_extract_metric -i mtsat.nii.gz -f label_axT1w/atlas -l 51 -vert 2:5 -vertfile label_axT1w/template/PAM50_levels.nii.gz -o ${PATH_RESULTS}/MTsat.csv -append 1
# # sct_extract_metric -i t1map.nii.gz -f label_axT1w/atlas -l 51 -vert 2:5 -vertfile label_axT1w/template/PAM50_levels.nii.gz -o ${PATH_RESULTS}/T1.csv -append 1
# # # Compute MTR in LCST between C2 and C5 vertebral levels
# # sct_extract_metric -i mtr.nii.gz -f label_axT1w/atlas -l 2,17 -vert 2:5 -vertfile label_axT1w/template/PAM50_levels.nii.gz -o ${PATH_RESULTS}/MTR_LCST.csv -append 1 -combine 1
# # # Compute MTR in LCST between C2 and C5 vertebral levels
# # sct_extract_metric -i mtr.nii.gz -f label_axT1w/atlas -l 0,1,15,16 -vert 2:5 -vertfile label_axT1w/template/PAM50_levels.nii.gz -o ${PATH_RESULTS}/MTR_DC.csv -append 1 -combine 1
# else
# echo "WARNING: MTS dataset is incomplete."
# fi

# Display useful info for the log
end=`date +%s`
runtime=$((end-start))
Expand All @@ -402,63 +189,3 @@ echo "SCT version: `sct_version`"
echo "Ran on: `uname -nsr`"
echo "Duration: $(($runtime / 3600))hrs $((($runtime / 60) % 60))min $(($runtime % 60))sec"
echo "~~~"







# # # Add suffix corresponding to contrast
# # if [[ ! -f ${file}MT_MTS.nii.gz ]]; then
# # file=${file}_acq-MT_MTS
# # elif [[ ! -f ${file}_STIR.nii.gz ]]; then
# # file=${file}_STIR
# # elif [[ ! -f ${file}_T2star.nii.gz ]]; then
# # file=${file}_T2star
# # elif [[ ! -f ${file}_T2w.nii.gz ]];
# # file=${file}_T2w
# # elif [[ ! -f ${file}_PSIR.nii.gz ]]; then
# # file=${file}_PSIR
# # elif [[ ! -f ${file}T1w_MTS.nii.gz ]];
# # file=${file}_T1w_MTS
# # elif [[ ! -f ${file}T1w.nii.gz ]];
# # file=${file}_T1w
# # elif [[ ! -f ${file}MTon_MTS.nii.gz ]]; then
# # file=${file}_acq-MTon_MTS
# # else [[ ! -f ${file}MToff_MTS.nii.gz ]];
# # file=${file}_acq-MToff_MTS
# # fi

# # Make sure the image metadata is a valid JSON object
# if [[ ! -s ${file}.json ]]; then
# echo "{}" >> ${file}.json
# fi

# # Spinal cord segmentation using the appropriate image contrast
# # for fil in file;do
# # if [[ "$fil" == *"$T2w"* ]];then
# # segment_if_does_not_exist ${file} t2 ${CENTERLINE_METHOD}
# # file_seg="${FILESEG}"
# # elif [[ "$fil" == *"$T2star"* ]];then
# # segment_if_does_not_exist ${file} t2s ${CENTERLINE_METHOD}
# # file_seg="${FILESEG}"
# # elif [[ "$fil" == *"$T1w"* ]];then
# # segment_if_does_not_exist ${file} t1 ${CENTERLINE_METHOD}
# # file_seg="${FILESEG}"
# # else
# # echo "contrast not supported. Skipping"
# # fi

# segment_if_does_not_exist ${file} t2 ${CENTERLINE_METHOD}
# file_seg="${FILESEG}"

# # get image dims from nifti header info
# ## get header
# ## | = from that output, apply next function / call
# ### -f1 = select 1st col; -d ',' = using ',' as the delimiter
# #### grep dim = get all lines that have word 'dim'
# ##### sed -n '1p' = select first line. See https://riptutorial.com/sed/example/13752/specific-range-of-lines
# ###### -f2 = select 2nd col; -d '[' = delimited by '['
# ####### $( ) = assign to shell variable
# # dims=$(sct_image -i ${file}.nii.gz -header | cut -f1 -d ',' | grep dim | sed -n '1p' | cut -f2 -d '[')
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