Update batch_single_subject.sh and process_data.sh to match changes to 2024 SCT Course Slides

multi_subject/process_data.sh

@@ -80,11 +80,11 @@ segment_if_does_not_exist() {
   if [[ -e "${FILESEGMANUAL}" ]]; then
     echo "Found! Using manual segmentation."
     rsync -avzh "${FILESEGMANUAL}" "${FILESEG}".nii.gz
-    sct_qc -i "${file}".nii.gz -s "${FILESEG}".nii.gz -p sct_deepseg_sc -qc "${PATH_QC}" -qc-subject "${SUBJECT}"
+    sct_qc -i "${file}".nii.gz -s "${FILESEG}".nii.gz -p sct_deepseg -qc "${PATH_QC}" -qc-subject "${SUBJECT}"
   else
     echo "Not found. Proceeding with automatic segmentation."
     # Segment spinal cord
-    sct_deepseg_sc -i "${file}".nii.gz -c "${contrast}" -qc "${PATH_QC}" -qc-subject "${SUBJECT}"
+    sct_deepseg -task seg_sc_contrast_agnostic -i "${file}".nii.gz -qc "${PATH_QC}" -qc-subject "${SUBJECT}"
   fi
 }
 

single_subject/batch_single_subject.sh

@@ -36,13 +36,21 @@ fi
 # Spinal cord segmentation
 # ======================================================================================================================
 
-# Go to T2 contrast
+# Go to T2 folder
 cd data/t2
-# Spinal cord segmentation (
-sct_deepseg_sc -i t2.nii.gz -c t2 -qc ~/qc_singleSubj
+# Spinal cord segmentation (using the new 2024 contrast-agnostic method)
+sct_deepseg -task seg_sc_contrast_agnostic -i t2.nii.gz -qc ~/qc_singleSubj
+# The default output is t2_seg.nii.gz
+# You can also choose your own output filename using the “-o” argument
+# sct_deepseg -task seg_sc_contrast_agnostic -i t2.nii.gz -o t2_seg_test.nii.gz
+
 # To check the QC report, use your web browser to open the file qc_singleSubj/qc/index.html, which has been created in
 # your home directory
 
+# View the rest of the `sct_deepseg` tasks
+sct_deepseg -h
+# See also: https://spinalcordtoolbox.com/stable/user_section/command-line/sct_deepseg.html
+
 
 
 # Vertebral labeling
@@ -59,17 +67,6 @@ sct_label_vertebrae -i t2.nii.gz -s t2_seg.nii.gz -c t2 -qc ~/qc_singleSubj
 #sct_label_utils -i t2.nii.gz -create-viewer 3 -o label_c2c3.nii.gz -msg "Click at the posterior tip of C2/C3 inter-vertebral disc"
 #sct_label_vertebrae -i t2.nii.gz -s t2_seg.nii.gz -c t2 -initlabel label_c2c3.nii.gz -qc ~/qc_singleSubj
 
-# Create labels at C3 and T2 mid-vertebral levels. These labels are needed for template registration.
-sct_label_utils -i t2_seg_labeled.nii.gz -vert-body 3,9 -o t2_labels_vert.nii.gz
-# Generate a QC report to visualize the two selected labels on the anatomical image
-sct_qc -i t2.nii.gz -s t2_labels_vert.nii.gz -p sct_label_utils -qc ~/qc_singleSubj
-
-# OPTIONAL: You might want to completely bypass sct_label_vertebrae and do the labeling manually. In that case, we
-# provide a viewer to do so conveniently. In the example command below, we will create labels at the inter-vertebral
-# discs C2-C3 (value=3), C3-C4 (value=4) and C4-C5 (value=5).
-#sct_label_utils -i t2.nii.gz -create-viewer 3,4,5 -o labels_disc.nii.gz -msg "Place labels at the posterior tip of each inter-vertebral disc. E.g. Label 3: C2/C3, Label 4: C3/C4, etc."
-
-
 
 
 # Computing shape metrics
@@ -80,7 +77,7 @@ sct_process_segmentation -i t2_seg.nii.gz -vert 3:4 -vertfile t2_seg_labeled.nii
 # Aggregate CSA value per level
 sct_process_segmentation -i t2_seg.nii.gz -vert 3:4 -vertfile t2_seg_labeled.nii.gz -perlevel 1 -o csa_perlevel.csv
 # Aggregate CSA value per slices
-sct_process_segmentation -i t2_seg.nii.gz -z 30:35 -perslice 1 -o csa_perslice.csv
+sct_process_segmentation -i t2_seg.nii.gz -z 30:35 -vertfile t2_seg_labeled.nii.gz -perslice 1 -o csa_perslice.csv
 
 # A drawback of vertebral level-based CSA is that it doesn’t consider neck flexion and extension.
 # To overcome this limitation, the CSA can instead be computed using the distance to a reference point.
@@ -90,13 +87,17 @@ sct_detect_pmj -i t2.nii.gz -c t2 -qc ~/qc_singleSubj
 # Check the QC to make sure PMJ was properly detected, then compute CSA using the distance from the PMJ:
 sct_process_segmentation -i t2_seg.nii.gz -pmj t2_pmj.nii.gz -pmj-distance 64 -pmj-extent 30 -o csa_pmj.csv -qc ~/qc_singleSubj -qc-image t2.nii.gz
 
+# The above commands will output the metrics in the subject space (with the original image's slice numbers)
+# However, you can get the corresponding slice number in the PAM50 space by using the flag `-normalize-PAM50 1`
+sct_process_segmentation -i t2_seg.nii.gz -vertfile t2_seg_labeled.nii.gz -perslice 1 -normalize-PAM50 1 -o csa_PAM50.csv
 
 
-# Computing normalized shape metrics for compressed data
+
+# Quantifying spinal cord compression using maximum spinal cord compression (MSCC) and normalizing with database of healthy controls
 # ======================================================================================================================
 cd ../t2_compression
 # Segment the spinal cord of the compressed spine
-sct_deepseg_sc -i t2_compressed.nii.gz -c t2 -qc ~/qc_singleSubj
+sct_deepseg -task seg_sc_contrast_agnostic -i t2_compressed.nii.gz -qc ~/qc_singleSubj
 # Label the vertebrae using the compressed spinal cord segmentation
 sct_label_vertebrae -i t2_compressed.nii.gz -s t2_compressed_seg.nii.gz -c t2 -qc ~/qc_singleSubj
 # Generate labels for each spinal cord compression site.
@@ -146,10 +147,33 @@ fsleyes t2.nii.gz -cm greyscale t2_seg.nii.gz -cm subcortical -a 70.0 &
 # Registering T2 data to the PAM50 template
 # ======================================================================================================================
 cd ../t2
+
+# Create labels at C3 and T2 mid-vertebral levels. These labels are needed for template registration.
+sct_label_utils -i t2_seg_labeled.nii.gz -vert-body 3,9 -o t2_labels_vert.nii.gz
+# Generate a QC report to visualize the two selected labels on the anatomical image
+sct_qc -i t2.nii.gz -s t2_labels_vert.nii.gz -p sct_label_utils -qc ~/qc_singleSubj
+
+# OPTIONAL: You might want to completely bypass sct_label_vertebrae and do the labeling manually. In that case, we
+# provide a viewer to do so conveniently. In the example command below, we will create labels at the inter-vertebral
+# discs C2-C3 (value=3), C3-C4 (value=4) and C4-C5 (value=5).
+#sct_label_utils -i t2.nii.gz -create-viewer 3,4,5 -o labels_disc.nii.gz -msg "Place labels at the posterior tip of each inter-vertebral disc. E.g. Label 3: C2/C3, Label 4: C3/C4, etc."
+
 # Register t2->template.
 sct_register_to_template -i t2.nii.gz -s t2_seg.nii.gz -l t2_labels_vert.nii.gz -c t2 -qc ~/qc_singleSubj
 # Note: By default the PAM50 template is selected. You can also select your own template using flag -t.
 
+# Register t2->template with modified parameters (advanced usage of `-param`)
+sct_register_to_template -i t2.nii.gz -s t2_seg.nii.gz -l t2_labels_vert.nii.gz -qc ~/qc_singleSubj -ofolder advanced_param -c t2 -param step=1,type=seg,algo=rigid:step=2,type=seg,metric=CC,algo=bsplinesyn,slicewise=1,iter=3:step=3,type=im,metric=CC,algo=syn,slicewise=1,iter=2
+
+# Register t2->template with large FOV (e.g. C2-L1) using `-ldisc` option
+# sct_register_to_template -i t2.nii.gz -s t2_seg.nii.gz -ldisc t2_seg_labeled_discs.nii.gz -c t2
+
+# Register t2->template in compressed cord (example command)
+# In case of highly compressed cord, the algo columnwise can be used, which allows for more deformation than bsplinesyn.
+# NB: In the example below, the registration is done in the subject space (no straightening) using a single label point at disc C3-C4 (<LABEL_DISC>).
+# sct_register_to_template -i <IMAGE> -s <SEGMENTATION> -ldisc <LABEL_DISC> -ref subject -param step=1,type=seg,
+# algo=centermassrot:step=2,type=seg,algo=columnwise
+
 # Warp template objects (T2, cord segmentation, vertebral levels, etc.). Here we use -a 0 because we don’t need the
 # white matter atlas at this point.
 sct_warp_template -d t2.nii.gz -w warp_template2anat.nii.gz -a 0 -qc ~/qc_singleSubj
@@ -167,7 +191,7 @@ fsleyes t2.nii.gz -cm greyscale -a 100.0 label/template/PAM50_t2.nii.gz -cm grey
 # Go to mt folder
 cd ../mt
 # Segment cord
-sct_deepseg_sc -i mt1.nii.gz -c t2 -qc ~/qc_singleSubj
+sct_deepseg -task seg_sc_contrast_agnostic -i mt1.nii.gz -qc ~/qc_singleSubj
 
 # Create a close mask around the spinal cord for more accurate registration (i.e. does not account for surrounding
 # tissue which could move independently from the cord)
@@ -208,27 +232,6 @@ sct_compute_mtr -mt0 mt0_reg.nii.gz -mt1 mt1.nii.gz
 
 
 
-# Contrast-agnostic registration
-# ======================================================================================================================
-
-# 1. T2w preprocessing (cropping around spinal cord)
-cd ../t2
-sct_deepseg_sc -i t2.nii.gz -c t2 -qc ~/qc_singleSubj
-sct_create_mask -i t2.nii.gz -p centerline,t2_seg.nii.gz -size 35mm -f cylinder -o mask_t2.nii.gz
-sct_crop_image -i t2.nii.gz -m mask_t2.nii.gz
-
-# 2. T1w preprocessing (cropping around spinal cord)
-cd ../t1
-sct_deepseg_sc -i t1.nii.gz -c t1 -qc ~/qc_singleSubj
-sct_create_mask -i t1.nii.gz -p centerline,t1_seg.nii.gz -size 35mm -f cylinder -o mask_t1.nii.gz
-sct_crop_image -i t1.nii.gz -m mask_t1.nii.gz
-
-# 3. Perform registration
-# NB: `-dseg` is not necessary for registration, but is provided for the `-qc` reporting to help with spinal cord visualization
-sct_register_multimodal -i t1_crop.nii.gz -d ../t2/t2_crop.nii.gz -param step=1,type=im,algo=dl -qc ~/qc_singleSubj -dseg ../t2/t2_seg.nii.gz
-
-
-
 # Registering lumbar data to the PAM50 template
 # ======================================================================================================================
 cd ../t2_lumbar
@@ -243,7 +246,7 @@ sct_deepseg -i t2_lumbar.nii.gz -task seg_lumbar_sc_t2w
 #
 # However, since this is an automated script with example data, we will place the labels at known locations for the
 # sake of reproducing the results in the tutorial.
-sct_label_utils -i t2_lumbar.nii.gz -create 22,77,187,17:27,79,80,60 -o t2_lumbar_labels.nii.gz
+sct_label_utils -i t2_lumbar.nii.gz -create 27,76,187,17:27,79,80,60 -o t2_lumbar_labels.nii.gz
 
 # Register the image to the template using segmentation and labels
 sct_register_to_template -i t2_lumbar.nii.gz \
@@ -262,7 +265,7 @@ cd ../t2s
 # Segment gray matter (check QC report afterwards)
 sct_deepseg_gm -i t2s.nii.gz -qc ~/qc_singleSubj
 # Spinal cord segmentation
-sct_deepseg_sc -i t2s.nii.gz -c t2s -qc ~/qc_singleSubj
+sct_deepseg -task seg_sc_contrast_agnostic -i t2s.nii.gz -qc ~/qc_singleSubj
 # Subtract GM segmentation from cord segmentation to obtain WM segmentation
 sct_maths -i t2s_seg.nii.gz -sub t2s_gmseg.nii.gz -o t2s_wmseg.nii.gz
 
@@ -296,8 +299,9 @@ sct_register_multimodal -i "${SCT_DIR}"/data/PAM50/template/PAM50_t2s.nii.gz -is
 # Warp template
 sct_warp_template -d t2s.nii.gz -w warp_template2t2s.nii.gz -qc ~/qc_singleSubj
 
+# Register another metric while reusing newly-created GM-informed warping fields
 cd ../mt
-# Register template->mt via t2s to account for GM segmentation
+# Register template->mt using `-initwarp` with t2s to account for GM segmentation
 sct_register_multimodal -i "${SCT_DIR}"/data/PAM50/template/PAM50_t2.nii.gz -iseg "${SCT_DIR}"/data/PAM50/template/PAM50_cord.nii.gz -d mt1.nii.gz -dseg mt1_seg.nii.gz -param step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3 -m mask_mt1.nii.gz -initwarp ../t2s/warp_template2t2s.nii.gz -owarp warp_template2mt.nii.gz -qc ~/qc_singleSubj
 # Warp template
 sct_warp_template -d mt1.nii.gz -w warp_template2mt.nii.gz -qc ~/qc_singleSubj
@@ -330,16 +334,16 @@ cd ../dmri
 sct_dmri_separate_b0_and_dwi -i dmri.nii.gz -bvec bvecs.txt 
 # Segment SC on mean dMRI data
 # Note: This segmentation does not need to be accurate-- it is only used to create a mask around the cord
-sct_deepseg_sc -i dmri_dwi_mean.nii.gz -c dwi -qc ~/qc_singleSubj
+sct_deepseg -task seg_sc_contrast_agnostic -i dmri_dwi_mean.nii.gz -qc ~/qc_singleSubj
 # Create mask (for subsequent cropping)
-sct_create_mask -i dmri_dwi_mean.nii.gz -p centerline,dmri_dwi_mean_seg.nii.gz -f cylinder -size 35mm
+sct_create_mask -i dmri_dwi_mean.nii.gz -p centerline,dmri_dwi_mean_seg.nii.gz -size 35mm
 
 # Motion correction (moco)
 sct_dmri_moco -i dmri.nii.gz -m mask_dmri_dwi_mean.nii.gz -bvec bvecs.txt -qc ~/qc_singleSubj -qc-seg dmri_dwi_mean_seg.nii.gz
 # Check results in the QC report
 
 # Segment SC on motion-corrected mean dwi data (check results in the QC report)
-sct_deepseg_sc -i dmri_moco_dwi_mean.nii.gz -c dwi -qc ~/qc_singleSubj
+sct_deepseg -task seg_sc_contrast_agnostic -i dmri_moco_dwi_mean.nii.gz -qc ~/qc_singleSubj
 
 # Register template->dwi via t2s to account for GM segmentation
 # Tips: Here we use the PAM50 contrast t1, which is closer to the dwi contrast (although we are not using type=im in
@@ -392,15 +396,62 @@ sct_warp_template -d fmri_moco_mean.nii.gz -w warp_template2fmri.nii.gz -a 0 -qc
 
 cd ../t1
 # Segment T1-weighted image (to be used in later steps)
-sct_deepseg_sc -i t1.nii.gz -c t1
+sct_deepseg -task seg_sc_contrast_agnostic -i t1.nii.gz
 
 # Smooth spinal cord along centerline (extracted from the segmentation)
 sct_smooth_spinalcord -i t1.nii.gz -s t1_seg.nii.gz
 # Tips: use flag "-sigma" to specify smoothing kernel size (in mm)
 
+# Second-pass segmentation using the smoothed anatomical image
+sct_deepseg_sc -i t1_smooth.nii.gz -c t1 -qc ~/qc_singleSubj
+
 # Align the spinal cord in the right-left direction using slice-wise translations.
 sct_flatten_sagittal -i t1.nii.gz -s t1_seg.nii.gz
 # Note: Use for visualization purposes only
 
+
+
+# New features (SCT v6.5, December 2024)
+# ======================================================================================================================
+
+# Lesion analysis for SCI and MS lesions
+cd ../t2_lesion
+# Segment the spinal cord and intramedullary lesion using the SCIsegV2 model
+# Note: t2.nii.gz contains a fake lesion for the purpose of this tutorial
+sct_deepseg -i t2.nii.gz -task seg_sc_lesion_t2w_sci -qc ~/qc_singleSubj
+# Note: Two files are output:
+# - t2_sc_seg.nii.gz: the spinal cord segmentation
+# - t2_lesion_seg.nii.gz: the lesion segmentation
+# Check results using FSLeyes
+fsleyes t2.nii.gz -cm greyscale t2_sc_seg.nii.gz -cm red -a 70.0 t2_lesion_seg.nii.gz -cm blue-lightblue -a 70.0 &
+# Compute various morphometric measures, such as number of lesions, lesion length, lesion volume, etc.
+sct_analyze_lesion -m t2_lesion_seg.nii.gz -s t2_sc_seg.nii.gz -qc ~/qc_singleSubj
+
+# Lesion analysis using PAM50 (the -f flag is used to specify the folder containing the atlas/template)
+# Note: You must go through the "Register to Template" steps (labeling, registration) first
+# sct_analyze_lesion -m t2_lesion_seg.nii.gz -s t2_sc_seg.nii.gz -f label -qc ~/qc_singleSubj
+
+# Note: We also have a contrast-agnostic segmentation command for MS lesions, too:
+sct_deepseg -i t2.nii.gz -task seg_ms_lesion -qc ~/qc_singleSubj
+# As well as a segmentation command tailored to MP2RAGE MS lesions
+sct_deepseg -i t2.nii.gz -task seg_ms_lesion_mp2rage -qc ~/qc_singleSubj
+
+# Rootlets segmentation
+cd ../t2
+# Segment the spinal nerve rootlets
+sct_deepseg -i t2.nii.gz -task seg_spinal_rootlets_t2w -qc ~/qc_singleSubj
+# Check results using FSLeyes
+fsleyes t2.nii.gz -cm greyscale t2_rootlets.nii.gz -cm subcortical -a 70.0 &
+
+# Canal segmentation
+sct_deepseg -i t2.nii.gz -task canal_t2w -qc ~/qc_singleSubj
+# Check results using FSLeyes
+fsleyes t2.nii.gz -cm greyscale t2_canal_seg_seg.nii.gz -cm red -a 70.0 &
+
+# Full spinal segmentation (Vertebrae, Intervertebral discs, Spinal cord and Spinal canal)
+# Segment using totalspineseg
+sct_deepseg -i t2.nii.gz -task totalspineseg -qc ~/qc_singleSubj
+# Check results using FSLeyes
+fsleyes t2.nii.gz -cm greyscale t2_step1_canal.nii.gz -cm YlOrRd -a 70.0 t2_step1_cord.nii.gz -cm YlOrRd -a 70.0 t2_step1_levels.nii.gz -cm subcortical -a 70.0 t2_step1_output.nii.gz -cm subcortical -a 70.0 t2_step2_output.nii.gz -cm subcortical -a 70.0 &
 # Return to parent directory
 cd ..