diff --git a/python/mrtrix3/commands/5ttgen/deep_atropos.py b/python/mrtrix3/commands/5ttgen/deep_atropos.py
index 3e07d7711a..e90c917a18 100644
--- a/python/mrtrix3/commands/5ttgen/deep_atropos.py
+++ b/python/mrtrix3/commands/5ttgen/deep_atropos.py
@@ -14,13 +14,42 @@
 # For more details, see http://www.mrtrix.org/.
 
 import os
+import argparse 
 from mrtrix3 import MRtrixError
 from mrtrix3 import app, image, run
 
 def usage(base_parser, subparsers):  #pylint: disable=unused-variable
   parser = subparsers.add_parser('deep_atropos', parents=[base_parser])
   parser.set_author('Lucius S. Fekonja (lucius.fekonja[at]charite.de) and Robert E. Smith (robert.smith@florey.edu.au)')
-  parser.set_synopsis('Generate the 5TT image based on a Deep Atropos segmentation image')
+  parser.set_synopsis('Generate the 5TT image based on a Deep Atropos segmentation or probabilities image')
+  parser.add_citation('Use of the ANTsX ecosystem should be accompanied by the following citation:\n'
+                     'N.J. Tustison, P.A. Cook, A.J. Holbrook, H.J. Johnson, J. Muschelli, G.A. Devenyi, J.T. Duda, S.R. Das, '
+                     'N.C. Cullen, D.L. Gillen, M.A. Yassa, J.R. Stone, J.C. Gee, and B.B. Avants. '
+                     'The ANTsX ecosystem for quantitative biological and medical imaging. '
+                     'Scientific Reports, 11(1):9068 (2021), pp. 1-13.',
+                     is_external=True)
+  parser.add_description('This algorithm accepts either:')
+  parser.add_description('1. A 3D segmentation image from Deep Atropos with labels:')
+  parser.add_description('   - 0: Background - 1: CSF - 2: Gray Matter - 3: White Matter - 4: Deep Gray Matter - 5: Brain Stem - 6: Cerebellum')
+  parser.add_description('2. A 4D probability image (dimensions [x,y,z,7]) containing probability maps for each tissue class in the same order as above')
+  parser.add_description('To generate these input images using antspynet\'s deep_atropos function:')
+  parser.add_description('1. Install required packages: pip install antspynet nibabel')
+  parser.add_description('2. Use Python commands:')
+  parser.add_description('   # Load the T1-weighted image')
+  parser.add_description('   >>> import ants, antspynet')
+  parser.add_description('   >>> import nibabel as nib')
+  parser.add_description('   >>> import numpy as np')
+  parser.add_description('   >>> t1_image = ants.image_read(\'input_t1.nii.gz\')')
+  parser.add_description('   # Run Deep Atropos segmentation')
+  parser.add_description('   >>> segments = antspynet.deep_atropos(t1_image)')
+  parser.add_description('   # Save the segmentation')
+  parser.add_description('   >>> ants.image_write(segments[\'segmentation_image\'], \'segmentation.nii.gz\')')
+  parser.add_description('   # Stack and save probability maps')
+  parser.add_description('   >>> prob_maps = np.stack([np.array(img.numpy()) for img in segments[\'probability_images\']], axis=-1)')
+  parser.add_description('   >>> nib.save(nib.Nifti1Image(prob_maps, t1_image.affine), \'probabilities.nii.gz\')')
+  parser.add_description('The generated files can then be used with MRtrix3:')
+  parser.add_description('   5ttgen deep_atropos segmentation.nii.gz 5tt.mif')
+  parser.add_description('   5ttgen deep_atropos probabilities.nii.gz 5tt.mif')
   parser.add_argument('input',
     type=app.Parser.ImageIn(),
     help='The input Deep Atropos segmentation image')