@@ -63,36 +63,14 @@ $ tree ../datasets/ds000031/
6363```
6464
6565
66-
67-
68-
69- ### ** Step 1:** Generate mapping file.
70-
71- a. Generate a mapping file using the ` generate_mappings.py ` script.
72- b. Look at your mapping file. Make sure it's not empty. Edit it, if there are any special cases you'd like to account for.
73-
74- ```
75- $ python generate_mappings.py -i ../datasets/ds000031 -o ./examples
76- ====================
77- Dataset Summary
78- ====================
79- Total number of sessions with 'anat' directory in the dataset: 24
80- Sessions with 'anat' directory with at least one T1w scan: 22
81- Sessions without a T1w scan: 2
82- List of sessions without a T1w scan:
83- ['sub-01/ses-053', 'sub-01/ses-016']
84-
85- Please find the mapping file in JSON format and other helpful logs at /Users/arshithab/dsst-defacing-pipeline/examples
86- ```
87-
88- ### ** Step 2:** Deface scans
66+ ### ** Step 1:** Deface scans
8967Run ` dsst_defacing_wf.py ` script that calls on ` deface.py ` and ` register.py ` to deface scans in the dataset.
9068
9169#### Option 1: Serially
9270If you have a small dataset with less than 10 subjects, then it might be easiest to run the defacing algorithm serially.
9371
9472``` bash
95- python dsst_defacing_wf.py -i ../datasets/ds000031 -m examples/primary_to_others_mapping.json - o examples
73+ python dsst_defacing_wf.py -i ../datasets/ds000031 -o examples
9674```
9775
9876#### Option 2: Parallelly
@@ -103,7 +81,7 @@ for i in `ls -d ../datasets/toy/*`; do SUBJ=$(echo $i | sed 's|../datasets/toy/|
10381swarm -f ./examples/defacing_parallel.swarm --module afni,fsl --merge-output --logdir ./examples/swarm_log
10482```
10583
106- ### ** Step 3 :** Visually QC defaced scans.
84+ ### ** Step 2 :** Visually QC defaced scans.
10785
10886Visual QC defacing accuracy gallery https://raamana.github.io/visualqc/gallery_defacing.html
10987
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