diff --git a/.github/workflows/run_batch_script.yml b/.github/workflows/run_batch_script.yml
index 2794316..4553393 100644
--- a/.github/workflows/run_batch_script.yml
+++ b/.github/workflows/run_batch_script.yml
@@ -62,10 +62,26 @@ jobs:
             fi
         done
 
-    - name: Check that script executed without error
+    - name: Check results (no error logs)
+      if: always()
       run: |
         cd "${{ github.event.repository.name }}/multi_subject/output/log"
         [ "$(compgen -G "process_data_sub-0*.log")" ]        # Log files should exist
+
+    - name: Check results (no error logs)
+      if: always()
+      run: |
+        cd "${{ github.event.repository.name }}/multi_subject/output/log"
         [ ! "$(compgen -G "err.process_data_sub-0*.log")" ]  # Error files should NOT exist
-        grep -iF "warning" process_data_sub-01.log
-        grep -iF "error" process_data_sub-01.log
+
+    - name: Check results (warnings in log)
+      if: always()
+      run: |
+        cd "${{ github.event.repository.name }}/multi_subject/output/log"
+        # ! grep -iF "warning" process_data_sub-01.log
+
+    - name: Check results (errors in log)
+      if: always()
+      run: |
+        cd "${{ github.event.repository.name }}/multi_subject/output/log"
+        ! grep -iF "error" process_data_sub-01.log
diff --git a/multi_subject/process_data.sh b/multi_subject/process_data.sh
index d10fcb4..9fd2155 100755
--- a/multi_subject/process_data.sh
+++ b/multi_subject/process_data.sh
@@ -71,7 +71,6 @@ segment_if_does_not_exist() {
   #  This allows you to add manual segmentations on a subject-by-subject basis without disrupting the pipeline.
   ###
   local file="${1}"
-  local contrast="${2}"
   # Update global variable with segmentation file name
   FILESEG="${file}"_seg
   FILESEGMANUAL="${PATH_DATA}"/derivatives/labels/"${SUBJECT}"/anat/"${FILESEG}".nii.gz
@@ -84,7 +83,7 @@ segment_if_does_not_exist() {
   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
 }
 
@@ -111,8 +110,8 @@ rsync -avzh "${PATH_DATA}"/"${SUBJECT}" .
 # ======================================================================================================================
 cd "${SUBJECT}"/anat/
 file_t2="${SUBJECT}"_T2w
-# Segment spinal cord (only if it does not exist)
-segment_if_does_not_exist "${file_t2}" "t2"
+# Segment spinal cord (only if it does not exist) using contrast-agnostic model
+segment_if_does_not_exist "${file_t2}"
 file_t2_seg="${FILESEG}"
 # Create labels in the cord at C2 and C5 mid-vertebral levels (only if it does not exist)
 label_if_does_not_exist "${file_t2}" "${file_t2_seg}"
@@ -132,8 +131,8 @@ sct_process_segmentation -i "${file_t2_seg}".nii.gz -vert 2:3 -vertfile label_T2
 # ======================================================================================================================
 file_mt1="${SUBJECT}"_acq-MTon_MTS
 file_mt0="${SUBJECT}"_acq-MToff_MTS
-# Segment spinal cord
-segment_if_does_not_exist "${file_mt1}" "t2s"
+# Segment spinal cord using contrast-agnostic model
+segment_if_does_not_exist "${file_mt1}"
 file_mt1_seg="${FILESEG}"
 # Create mask
 sct_create_mask -i "${file_mt1}".nii.gz -p centerline,"${file_mt1_seg}".nii.gz -size 45mm
diff --git a/single_subject/batch_single_subject.sh b/single_subject/batch_single_subject.sh
index d2f2f5d..6f0a268 100755
--- a/single_subject/batch_single_subject.sh
+++ b/single_subject/batch_single_subject.sh
@@ -2,10 +2,17 @@
 #
 # Example of commands to process multi-parametric data of the spinal cord
 # For information about acquisition parameters, see: www.spinalcordmri.org/protocols
-# N.B. The parameters were chosen to suit SCT's sample tutorial data. With your data,
-# it is worthwhile to explore the various parameters and tweak them to your situation.
 #
-# tested with Spinal Cord Toolbox (v6.1.0)
+# Notes:
+#   - Many of the commands in this script are commented out (start with "# "). These commands won't be run by default,
+#     as many of them involve manual steps (e.g. via an interactive pop-up interface), or may involve data-specific
+#     configuration. Often these commands correspond to the "try at home" slides in the SCT Course slide deck,
+#     (denoted by a "red circle" in the top-right corner of the corresponding slide). Feel free to "uncomment" the
+#     commands by removing the "# " symbol at the start of the line, and experiment with them on your own data.
+#   - The parameters were chosen to suit SCT's sample tutorial data. With your data,
+#     it is worthwhile to explore the various parameters and tweak them to your situation.
+#
+# tested with Spinal Cord Toolbox (v6.5)
 
 # Script utilities
 # ======================================================================================================================
@@ -36,13 +43,21 @@ 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 test/t2_seg_2.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
@@ -59,20 +74,9 @@ 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
+# Shape-based analysis
 # ======================================================================================================================
 
 # Compute cross-sectional area (CSA) of spinal cord and average it across levels C3 and C4
@@ -80,7 +84,7 @@ 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 +94,17 @@ 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.
@@ -114,42 +122,36 @@ sct_compute_compression -i t2_compressed_seg.nii.gz -vertfile t2_compressed_seg_
 
 
 
-# Lesion analysis
-# ======================================================================================================================
-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
-
-
-
-# Rootlets segmentation
+# Registration to template
 # ======================================================================================================================
 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_seg.nii.gz -cm subcortical -a 70.0 &
 
+# 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."
 
-# Registering T2 data to the PAM50 template
-# ======================================================================================================================
-cd ../t2
 # 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
@@ -161,13 +163,13 @@ fsleyes t2.nii.gz -cm greyscale -a 100.0 label/template/PAM50_t2.nii.gz -cm grey
 
 
 
-# Registering additional MT data to the PAM50 template
+# Registering additional contrasts (MT registration to T2 template)
 # ======================================================================================================================
 
 # 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)
@@ -194,7 +196,7 @@ fsleyes mt1.nii.gz -cm greyscale -a 100.0 label/template/PAM50_t2.nii.gz -cm gre
 
 
 
-# Computing MTR using MT0/MT1 coregistration
+# Registering additional contrasts (MT0/MT1 coregistration to compute MTR)
 # ======================================================================================================================
 
 # Register mt0->mt1 using z-regularized slicewise translations (algo=slicereg)
@@ -208,33 +210,12 @@ 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
+# Registering additional contrasts (T2 lumbar data)
 # ======================================================================================================================
 cd ../t2_lumbar
 
 # Use lumbar-specific `sct_deepseg` model to segment the spinal cord
-sct_deepseg -i t2_lumbar.nii.gz -task seg_lumbar_sc_t2w
+sct_deepseg -i t2_lumbar.nii.gz -task seg_lumbar_sc_t2w -qc ~/qc_singleSubj
 
 # Generate labels for the 2 spinal cord landmarks: cauda equinea ('99') and T9-T10 disc ('17')
 # Note: Normally this would be done manually using fsleyes' "Edit mode -> Create mask" functionality. (Uncomment below)
@@ -243,18 +224,14 @@ 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 -qc ~/qc_singleSubj
 
 # Register the image to the template using segmentation and labels
-sct_register_to_template -i t2_lumbar.nii.gz \
-                         -s t2_lumbar_seg.nii.gz \
-                         -ldisc t2_lumbar_labels.nii.gz \
-                         -c t2 -qc qc \
-                         -param step=1,type=seg,algo=centermassrot:step=2,type=seg,algo=bsplinesyn,metric=MeanSquares,iter=3,slicewise=0:step=3,type=im,algo=syn,metric=CC,iter=3,slicewise=0
+sct_register_to_template -i t2_lumbar.nii.gz -s t2_lumbar_seg.nii.gz -ldisc t2_lumbar_labels.nii.gz -c t2 -qc ~/qc_singleSubj -param step=1,type=seg,algo=centermassrot:step=2,type=seg,algo=bsplinesyn,metric=MeanSquares,iter=3,slicewise=0:step=3,type=im,algo=syn,metric=CC,iter=3,slicewise=0
 
 
 
-# Gray/white matter: Segmentation
+# Gray matter segmentation (GM/WM seg)
 # ======================================================================================================================
 
 # Go to T2*-weighted data, which has good GM/WM contrast and high in-plane resolution
@@ -262,13 +239,16 @@ 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
+# Note that we use the flag -thr 0 in case some voxels in the GM segmentation are *not* included in the cord
+# segmentation. That would results in voxels in the WM segmentation having the value “-1”, which would cause issues
+# with the registration. 
+sct_maths -i t2s_seg.nii.gz -sub t2s_gmseg.nii.gz -thr 0 -o t2s_wmseg.nii.gz
 
 
 
-# Gray/white matter: Computing metrics using binary segmentation masks
+# Gray matter segmentation (Shape-based analysis and metric extraction)
 # ======================================================================================================================
 
 # Compute cross-sectional area (CSA) of the gray and white matter for all slices in the volume.
@@ -287,7 +267,7 @@ sct_extract_metric -i t2s.nii.gz -f t2s_gmseg.nii.gz -method bin -z 2:12 -o t2s_
 
 
 
-# Gray/white matter: Improving registration results using binary segmentation masks
+# Gray matter segmentation (Improving registration results using binary segmentation masks)
 # ======================================================================================================================
 
 # Register template->t2s (using warping field generated from template<->t2 registration)
@@ -296,8 +276,9 @@ 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
@@ -330,23 +311,23 @@ 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
+# Register template->dwi via t2 to account for cord shape (which is better defined in T2 contrast)
 # Tips: Here we use the PAM50 contrast t1, which is closer to the dwi contrast (although we are not using type=im in
 #       -param, so it will not make a difference here)
 # Note: the flag “-initwarpinv" provides a transformation dmri->template, in case you would like to bring all your DTI
 #       metrics in the PAM50 space (e.g. group averaging of FA maps)
-sct_register_multimodal -i "${SCT_DIR}"/data/PAM50/template/PAM50_t1.nii.gz -iseg "${SCT_DIR}"/data/PAM50/template/PAM50_cord.nii.gz -d dmri_moco_dwi_mean.nii.gz -dseg dmri_moco_dwi_mean_seg.nii.gz -initwarp ../t2s/warp_template2t2s.nii.gz -initwarpinv ../t2s/warp_t2s2template.nii.gz -owarp warp_template2dmri.nii.gz -owarpinv warp_dmri2template.nii.gz -param step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3 -qc ~/qc_singleSubj
+sct_register_multimodal -i "${SCT_DIR}"/data/PAM50/template/PAM50_t1.nii.gz -iseg "${SCT_DIR}"/data/PAM50/template/PAM50_cord.nii.gz -d dmri_moco_dwi_mean.nii.gz -dseg dmri_moco_dwi_mean_seg.nii.gz -initwarp ../t2/warp_template2anat.nii.gz -initwarpinv ../t2/warp_anat2template.nii.gz -owarp warp_template2dmri.nii.gz -owarpinv warp_dmri2template.nii.gz -param step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3 -qc ~/qc_singleSubj
 # Warp template (so 'label/atlas' can be used to extract metrics)
 sct_warp_template -d dmri_moco_dwi_mean.nii.gz -w warp_template2dmri.nii.gz -qc ~/qc_singleSubj
 # Check results in the QC report
@@ -375,12 +356,19 @@ sct_create_mask -i fmri.nii.gz -p centerline,t2_seg_reg.nii.gz -size 35mm -f cyl
 # Motion correction (using mask)
 sct_fmri_moco -i fmri.nii.gz -m mask_fmri.nii.gz -qc ~/qc_singleSubj -qc-seg t2_seg_reg.nii.gz
 
+# Cord segmentation on motion-corrected averaged time series
+sct_deepseg -i fmri_moco_mean.nii.gz -task seg_sc_contrast_agnostic -qc ~/qc_singleSubj/
+# TSNR before/after motion correction with QC report
+sct_fmri_compute_tsnr -i fmri.nii.gz
+sct_fmri_compute_tsnr -i fmri_moco.nii.gz
+sct_qc -i fmri_tsnr.nii.gz -d fmri_moco_tsnr.nii.gz -s fmri_moco_mean_seg.nii.gz -p sct_fmri_compute_tsnr -qc ~/qc_singleSubj/
+
 # Register the template to the fMRI scan.
 # Note: here we don't rely on the segmentation because it is difficult to obtain one automatically. Instead, we rely on
 #       ANTs_SyN superpower to find a suitable transformation between the PAM50_t2s and the fMRI scan. We don't want to
 #       put too many iterations because this registration is very sensitive to the artifacts (drop out) in the image.
 #       Also, we want a 3D transformation (not 2D) because we need the through-z regularization.
-sct_register_multimodal -i "${SCT_DIR}/data/PAM50/template/PAM50_t2s.nii.gz" -d fmri_moco_mean.nii.gz -dseg t2_seg_reg.nii.gz -param step=1,type=im,algo=syn,metric=CC,iter=5,slicewise=0 -initwarp ../t2s/warp_template2t2s.nii.gz -initwarpinv ../t2s/warp_t2s2template.nii.gz -owarp warp_template2fmri.nii.gz -owarpinv warp_fmri2template.nii.gz -qc ~/qc_singleSubj
+sct_register_multimodal -i "${SCT_DIR}"/data/PAM50/template/PAM50_t2s.nii.gz -iseg "${SCT_DIR}"/data/PAM50/template/PAM50_cord.nii.gz -d fmri_moco_mean.nii.gz -dseg fmri_moco_mean_seg.nii.gz -param step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3:step=3,type=im,algo=syn,metric=CC,iter=3,slicewise=1 -initwarp ../t2/warp_template2anat.nii.gz -initwarpinv ../t2/warp_anat2template.nii.gz -owarp warp_template2fmri.nii.gz -owarpinv warp_fmri2template.nii.gz -qc ~/qc_singleSubj
 # Check results in the QC report
 
 # Warp template with the spinal levels (can be found at $SCT_DIR/data/PAM50/template/)
@@ -392,15 +380,67 @@ 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 -qc ~/qc_singleSubj/
 
 # 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 &
+
+# 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
+
+# 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
+#       This is because `sct_warp_template` is required to generate the `label` folder used for `-f`
+# sct_analyze_lesion -m t2_lesion_seg.nii.gz -s t2_sc_seg.nii.gz -f label -qc ~/qc_singleSubj
+
+# Segment the spinal cord on gradient echo EPI data
+cd ../fmri/
+sct_deepseg -i fmri_moco_mean.nii.gz -task seg_sc_epi -qc ~/qc_singleSubj
+
+# Canal segmentation
+cd ../t2
+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 &
+
+# 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 &
+
 # Return to parent directory
 cd ..