5ttgen deep_atropos: Fixes to example usages

- Fix use of single vs double quotes so that examples can be copy-pasted into a terminal and executed successfully. - Load the input image a second time using nibabel to get the affine transformation; the ANTsImage class modifies both the content and the representation of this information, and does not contain a member variable ".affine" as utilised in the original example.
MRtrix3 · Jan 21, 2025 · ee90596 · ee90596
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diff --git a/docs/reference/commands/5ttgen.rst b/docs/reference/commands/5ttgen.rst
@@ -122,13 +122,13 @@ Example usages
 
 -   *To utilise the "segmentation" image*::
 
-        $ python3 -c 'import ants, antspynet; t1w = ants.image_read('T1w.nii.gz'); result = antspynet.deep_atropos(t1w); ants.image_write'result['segmentation_image'], 'segmentation.nii.gz')'; 5ttgen deep_atropos segmentation.nii.gz 5tt_segmentation.mif
+        $ python3 -c "import ants, antspynet; t1w = ants.image_read('T1w.nii.gz'); result = antspynet.deep_atropos(t1w); ants.image_write(result['segmentation_image'], 'segmentation.nii.gz')"; 5ttgen deep_atropos segmentation.nii.gz 5tt_segmentation.mif
 
     Because the input segmentation here is an integer image, where each voxel just contains an index corresponding to the maximal tissue class, the output 5TT image will not possess any fractional partial volumes; it will just contain the value 1.0 in whichever 5TT volume corresponds to the singular assigned tissue class.
 
 -   *To utilise the "probability images"*::
 
-        $ python3 -c 'import ants, antspynet, nibabel, numpy; t1w = ants.image_read('T1w.nii.gz'); result = antspynet.deep_atropos(t1w); prob_maps = numpy.stack([numpy.array(img.numpy()) for img in result['probability_images']], axis=-1); nibabel.save(nib.Nifti1Image(prob_maps, t1w.affine), 'probabilities.nii.gz')'; 5ttgen deep_atropos probabilities.nii.gz 5tt_probabilities.mif
+        $ python3 -c "import ants, antspynet, nibabel, numpy; inpath = 'T1w.nii.gz'; t1w_ants = ants.image_read(inpath); t1w_nib = nibabel.load(inpath); result = antspynet.deep_atropos(t1w_ants); prob_maps = numpy.stack([numpy.array(img.numpy()) for img in result['probability_images']], axis=-1); nibabel.save(nibabel.Nifti1Image(prob_maps, t1w_nib.affine), 'probabilities.nii.gz')"; 5ttgen deep_atropos probabilities.nii.gz 5tt_probabilities.mif
 
     In this use case, the poerior probabilities of these tissue classes are interpreted as partial volume fractions and imported into the derivative 5TT image appropriately.
 

diff --git a/python/mrtrix3/commands/5ttgen/deep_atropos.py b/python/mrtrix3/commands/5ttgen/deep_atropos.py
@@ -40,21 +40,23 @@ def usage(base_parser, subparsers):  #pylint: disable=unused-variable
                          'require that "ants" and "antspynet" be installed via Python\'s "pip"; '
                          'use of the "probability images" also requires that nibabel and numpy be installed.')
   parser.add_example_usage('To utilise the "segmentation" image',
-                           'python3 -c \'import ants, antspynet; '
-                                        't1w = ants.image_read(\'T1w.nii.gz\'); '
-                                        'result = antspynet.deep_atropos(t1w); '
-                                        'ants.image_write\'result[\'segmentation_image\'], \'segmentation.nii.gz\')\'; '
+                           'python3 -c "import ants, antspynet; '
+                                       't1w = ants.image_read(\'T1w.nii.gz\'); '
+                                       'result = antspynet.deep_atropos(t1w); '
+                                       'ants.image_write(result[\'segmentation_image\'], \'segmentation.nii.gz\')"; '
                            '5ttgen deep_atropos segmentation.nii.gz 5tt_segmentation.mif',
                            'Because the input segmentation here is an integer image, '
                            'where each voxel just contains an index corresponding to the maximal tissue class, '
                            'the output 5TT image will not possess any fractional partial volumes; '
                            'it will just contain the value 1.0 in whichever 5TT volume corresponds to the singular assigned tissue class.')
   parser.add_example_usage('To utilise the "probability images"',
-                           'python3 -c \'import ants, antspynet, nibabel, numpy; '
-                                        't1w = ants.image_read(\'T1w.nii.gz\'); '
-                                        'result = antspynet.deep_atropos(t1w); '
-                                        'prob_maps = numpy.stack([numpy.array(img.numpy()) for img in result[\'probability_images\']], axis=-1); '
-                                        'nibabel.save(nib.Nifti1Image(prob_maps, t1w.affine), \'probabilities.nii.gz\')\'; '
+                           'python3 -c "import ants, antspynet, nibabel, numpy; '
+                                       'inpath = \'T1w.nii.gz\'; '
+                                       't1w_ants = ants.image_read(inpath); '
+                                       't1w_nib = nibabel.load(inpath); '
+                                       'result = antspynet.deep_atropos(t1w_ants); '
+                                       'prob_maps = numpy.stack([numpy.array(img.numpy()) for img in result[\'probability_images\']], axis=-1); '
+                                       'nibabel.save(nibabel.Nifti1Image(prob_maps, t1w_nib.affine), \'probabilities.nii.gz\')"; '
                            '5ttgen deep_atropos probabilities.nii.gz 5tt_probabilities.mif',
                            'In this use case, the poerior probabilities of these tissue classes are interpreted as partial volume fractions '
                            'and imported into the derivative 5TT image appropriately.')