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@@ -3,7 +3,8 @@
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set -o errexit
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-export ANTSPATH=/home/john/local/ANTs/install/bin/
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+export ANTSPATH=<your ANTS path>
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+export SUPPORTPATH=<your path to the supporting tools>
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if (( $# != 3)) ; then
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@@ -18,28 +19,21 @@ PREDICTION=$3
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TEMPDIR=TMP-$$
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mkdir -p ${TEMPDIR}
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-# ~/local/ANTs/install/bin/ImageMath 3 Mean.nii.gz ThresholdAtMean subject.nii
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-# I think it might be better to just make the whole image a mask
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fslmaths ${T2} -mul ${T2} -mul ${T2} -mul ${T2} ${TEMPDIR}/t2t2t2t2.nii.gz
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-~/local/ANTs/install/bin/ImageMath 3 ${TEMPDIR}/maskish.nii.gz ThresholdAtMean ${TEMPDIR}/t2t2t2t2.nii.gz
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-# fslmaths ${T1} -add 10000 -bin ${TEMPDIR}/maskish.nii.gz
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+${ANTSPATH}/ImageMath 3 ${TEMPDIR}/maskish.nii.gz ThresholdAtMean ${TEMPDIR}/t2t2t2t2.nii.gz
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-# mkdir -p /mnt/md0/Academia/Research/Projects/HIE/Cohort1/BET-CNN/Subjects/0/
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-
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-python /mnt/md0/Academia/Research/Projects/HIE/Cohort1/Pipelines/betcrop.py ${T1} ${T2} ${TEMPDIR}/maskish.nii.gz ${TEMPDIR}/SUBJ
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+python ${SUPPORTPATH}/betcrop.py ${T1} ${T2} ${TEMPDIR}/maskish.nii.gz ${TEMPDIR}/SUBJ
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cp -p ${TEMPDIR}/SUBJ_cropped_mask.nii.gz ${TEMPDIR}/SUBJ_cropped_target.nii.gz
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cd ${TEMPDIR}
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-# gunzip SUBJ_cropped_prediction.nii.gz
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-# then provide those to the BET-CNN to generate the prediction of the brain mask
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echo "`readlink -f .`/SUBJ_cropped_t1.nii.gz,`readlink -f .`/SUBJ_cropped_t2.nii.gz,`readlink -f .`/SUBJ_cropped" | tee files.csv
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-python /mnt/md0/Academia/Research/Projects/HIE/Cohort1/BET-CNN/models/BET-CNN.py files.csv
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+python ${SUPPORTPATH}/BET-CNN.py files.csv
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param2xfm -scales 0.5 0.5 0.5 scale.xfm
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mri_convert --upsample 2 --apply_transform scale.xfm -i SUBJ_cropped_bett1.nii -o SUBJ_cropped_hires_t1.nii.gz
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@@ -49,11 +43,10 @@ CMD="mri_convert -rt nearest --upsample 2 --apply_transform scalerightup.xfm -
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cd ..
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-/mnt/md0//local/ANTs/install/bin/antsRegistrationMISyN.sh -f ${T1} -m ${TEMPDIR}/SUBJ_cropped_hires_t1.nii.gz -o ${TEMPDIR}/SUBJ_cropped_t1_ants_ -t t ;
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+${ANTSPATH}/antsRegistrationMISyN.sh -f ${T1} -m ${TEMPDIR}/SUBJ_cropped_hires_t1.nii.gz -o ${TEMPDIR}/SUBJ_cropped_t1_ants_ -t t ;
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-# mri_convert -rt nearest --upsample 2 --apply_transform ${TEMPDIR}/scale.xfm -i ${TEMPDIR}/SUBJ_cropped_hires_prediction.nii.gz -o ${TEMPDIR}/SUBJ_cropped_hires_prediction.nii.gz
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-/mnt/md0/local/ANTs/install/bin/antsApplyTransforms -d 3 -t ${TEMPDIR}/SUBJ_cropped_t1_ants_0GenericAffine.mat -r ${T1} -i ${TEMPDIR}/SUBJ_cropped_hires_prediction.nii.gz -o ${PREDICTION} -n nearestneighbor
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+${ANTSPATH}/antsApplyTransforms -d 3 -t ${TEMPDIR}/SUBJ_cropped_t1_ants_0GenericAffine.mat -r ${T1} -i ${TEMPDIR}/SUBJ_cropped_hires_prediction.nii.gz -o ${PREDICTION} -n nearestneighbor
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echo -e "\n\nComplete!\n\n"
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