Basic Nipype Example

Author: Steffen Bollmann

Setup Neurodesk

import os
import sys
IN_COLAB = 'google.colab' in sys.modules

if IN_COLAB:
  os.environ["LD_PRELOAD"] = "";
  os.environ["APPTAINER_BINDPATH"] = "/content,/tmp,/cvmfs"
  os.environ["MPLCONFIGDIR"] = "/content/matplotlib-mpldir"
  os.environ["LMOD_CMD"] = "/usr/share/lmod/lmod/libexec/lmod"

  !curl -J -O https://raw.githubusercontent.com/NeuroDesk/neurocommand/main/googlecolab_setup.sh
  !chmod +x googlecolab_setup.sh
  !./googlecolab_setup.sh

  os.environ["MODULEPATH"] = ':'.join(map(str, list(map(lambda x: os.path.join(os.path.abspath('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/'), x),os.listdir('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/')))))

  from google.colab import output
  output.enable_custom_widget_manager()
  !pip install ipyniivue

%%bash
# Output CPU information:
cat /proc/cpuinfo | grep 'vendor' | uniq
cat /proc/cpuinfo | grep 'model name' | uniq

Demonstrating the module system in Python and Nipype

# we can use lmod to load fsl in a specific version
import lmod
await lmod.load('fsl/6.0.4')
await lmod.list()

['fsl/6.0.4']

from nipype.interfaces.fsl.base import Info
print(Info.version())
# if the FSL version is changed using lmod above, the kernel of the notebook needs to be restarted!

6.0.4:ddd0a010

!bet

Usage:    bet <input> <output> [options]

Main bet2 options:
  -o          generate brain surface outline overlaid onto original image
  -m          generate binary brain mask
  -s          generate approximate skull image
  -n          don't generate segmented brain image output
  -f <f>      fractional intensity threshold (0->1); default=0.5; smaller values give larger brain outline estimates
  -g <g>      vertical gradient in fractional intensity threshold (-1->1); default=0; positive values give larger brain outline at bottom, smaller at top
  -r <r>      head radius (mm not voxels); initial surface sphere is set to half of this
  -c <x y z>  centre-of-gravity (voxels not mm) of initial mesh surface.
  -t          apply thresholding to segmented brain image and mask
  -e          generates brain surface as mesh in .vtk format

Variations on default bet2 functionality (mutually exclusive options):
  (default)   just run bet2
  -R          robust brain centre estimation (iterates BET several times)
  -S          eye & optic nerve cleanup (can be useful in SIENA - disables -o option)
  -B          bias field & neck cleanup (can be useful in SIENA)
  -Z          improve BET if FOV is very small in Z (by temporarily padding end slices)
  -F          apply to 4D FMRI data (uses -f 0.3 and dilates brain mask slightly)
  -A          run bet2 and then betsurf to get additional skull and scalp surfaces (includes registrations)
  -A2 <T2>    as with -A, when also feeding in non-brain-extracted T2 (includes registrations)

Miscellaneous options:
  -v          verbose (switch on diagnostic messages)
  -h          display this help, then exits
  -d          debug (don't delete temporary intermediate images)

Load afni and SPM as well

await lmod.load('afni/22.3.06')
await lmod.load('spm12/r7771')
await lmod.list()

['fsl/6.0.4', 'afni/22.3.06', 'spm12/r7771']

download test data

%%bash
if [ -f ./sub-01_ses-01_7T_T1w_defaced.nii ]; then
    echo "nii Output file exists, not downloading or unpacking again"
else
    if [ ! -f ./sub-01_ses-01_7T_T1w_defaced.nii.gz  ]; then
        echo "nii.gz does not exist. So, it needs to be downloaded."
        osfURL="osfstorage/TOMCAT_DIB/sub-01/ses-01_7T/anat/sub-01_ses-01_7T_T1w_defaced.nii.gz"
        echo "downloading now ..."
        osf -p bt4ez fetch $osfURL ./sub-01_ses-01_7T_T1w_defaced.nii.gz
    fi

    if [  -f ./sub-01_ses-01_7T_T1w_defaced.nii.gz ]; then
        echo "nii.gz exists. So, it needs to be unpacked and deleted"
        echo "unpacking now ..."
        gunzip ./sub-01_ses-01_7T_T1w_defaced.nii.gz
    fi
fi

nii.gz does not exist. So, it needs to be downloaded.
downloading now ...

100%|██████████| 72.7M/72.7M [00:01<00:00, 63.0Mbytes/s]

nii.gz exists. So, it needs to be unpacked and deleted
unpacking now ...

%ls 

freesurfer_example.ipynb     sct_toolbox_example.ipynb
nipype_module_example.ipynb  sub-01_ses-01_7T_T1w_defaced.nii
qsmxt_example.ipynb

run nipype pipeline

%%capture
!pip install nipype

from nipype.interfaces import fsl
from nipype.interfaces import afni

btr = fsl.BET()
btr.inputs.in_file = './sub-01_ses-01_7T_T1w_defaced.nii'
btr.inputs.frac = 0.4
btr.inputs.out_file = './sub-01_ses-01_7T_T1w_defaced_brain.nii'
res = btr.run() 

edge3 = afni.Edge3()
edge3.inputs.in_file = './sub-01_ses-01_7T_T1w_defaced.nii'
edge3.inputs.out_file = './sub-01_ses-01_7T_T1w_defaced_edges.nii'
edge3.inputs.datum = 'byte'
res = edge3.run()

240319-22:30:36,502 nipype.interface WARNING:
	 FSLOUTPUTTYPE environment variable is not set. Setting FSLOUTPUTTYPE=NIFTI
240319-22:30:42,604 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.604177:++ 3dedge3: AFNI version=AFNI_22.3.06 (Nov 15 2022) [64-bit]
240319-22:30:42,609 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.609808:** AFNI converts NIFTI_datatype=4 (INT16) in file /home/jovyan/example-notebooks/books/structural_imaging/sub-01_ses-01_7T_T1w_defaced.nii to FLOAT32
240319-22:30:42,610 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.609808:     Warnings of this type will be muted for this session.
240319-22:30:42,611 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.609808:     Set AFNI_NIFTI_TYPE_WARN to YES to see them all, NO to see none.
240319-22:30:42,621 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.621294:*+ WARNING:   If you are performing spatial transformations on an oblique dset,
240319-22:30:42,622 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.621294:  such as /home/jovyan/example-notebooks/books/structural_imaging/sub-01_ses-01_7T_T1w_defaced.nii,
240319-22:30:42,623 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.621294:  or viewing/combining it with volumes of differing obliquity,
240319-22:30:42,624 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.621294:  you should consider running: 
240319-22:30:42,628 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.621294:     3dWarp -deoblique 
240319-22:30:42,629 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.621294:  on this and  other oblique datasets in the same session.
240319-22:30:42,629 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.621294: See 3dWarp -help for details.
240319-22:30:42,630 nipype.interface INFO:
	 stderr 2024-03-19T22:30:42.630248:++ Oblique dataset:/home/jovyan/example-notebooks/books/structural_imaging/sub-01_ses-01_7T_T1w_defaced.nii is 1.253358 degrees from plumb.

%ls

freesurfer_example.ipynb     sub-01_ses-01_7T_T1w_defaced.nii
nipype_module_example.ipynb  sub-01_ses-01_7T_T1w_defaced_brain.nii.gz
qsmxt_example.ipynb          sub-01_ses-01_7T_T1w_defaced_edges.nii
sct_toolbox_example.ipynb

# View 3D data
import matplotlib.pyplot as plt

def view_slices_3d(image_3d, slice_nbr, vmin, vmax, title=''):
#   print('Matrix size: {}'.format(image_3d.shape))
  fig = plt.figure(figsize=(15, 4))
  plt.suptitle(title, fontsize=10)

  plt.subplot(131)
  plt.imshow(np.take(image_3d, slice_nbr, 2), vmin=vmin, vmax=vmax, cmap='gray')
  plt.title('Axial');

  plt.subplot(132)
  image_rot = ndimage.rotate(np.take(image_3d, slice_nbr, 1),90)
  plt.imshow(image_rot, vmin=vmin, vmax=vmax, cmap='gray')
  plt.title('Coronal');

  plt.subplot(133)
  image_rot = ndimage.rotate(np.take(image_3d, slice_nbr, 0),90)
  plt.imshow(image_rot, vmin=vmin, vmax=vmax, cmap='gray')
  plt.title('Sagittal');
  cbar=plt.colorbar()

def get_figure():
    """
    Returns figure and axis objects to plot on. 
    """
    fig, ax = plt.subplots(1)
    plt.tick_params(top=False, right=False, which='both') 
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    return fig, ax

import nibabel as nib
from matplotlib import transforms
from scipy import ndimage
import numpy as np

# load data
brain_full = nib.load('./sub-01_ses-01_7T_T1w_defaced.nii').get_fdata()
brain = nib.load('./sub-01_ses-01_7T_T1w_defaced_brain.nii.gz').get_fdata()
edges = nib.load('./sub-01_ses-01_7T_T1w_defaced_edges.nii').get_fdata()

view_slices_3d(brain_full, slice_nbr=230, vmin=0, vmax=4000, title='Brain and Skull')
view_slices_3d(brain, slice_nbr=230, vmin=0, vmax=4000, title='Brain Extracted')
view_slices_3d(edges, slice_nbr=230, vmin=0, vmax=1000, title='Edges')

import ipyniivue
nv = ipyniivue.Niivue(crosshair_color=[0,1,0,1])
nv.add_volume('./sub-01_ses-01_7T_T1w_defaced_brain.nii.gz')
nv

SPM can also be used in such a workflow, but unfortunately, this will trigger a warning “stty: ‘standard input’: Inappropriate ioctl for device”, which you can ignore (or help us to find out where it comes from):

import nipype.interfaces.spm as spm

norm12 = spm.Normalize12()
norm12.inputs.image_to_align = './sub-01_ses-01_7T_T1w_defaced.nii'
norm12.run()

stty: 'standard input': Inappropriate ioctl for device

path:/opt/spm12/spm12_mcr/spm12/spm12|name:SPM12|release:7771

stty: 'standard input': Inappropriate ioctl for device

<nipype.interfaces.base.support.InterfaceResult at 0xffff385a1250>

brain_full = nib.load('./wsub-01_ses-01_7T_T1w_defaced.nii').get_fdata()
view_slices_3d(brain_full, slice_nbr=50, vmin=0, vmax=4000, title='Brain normalized to MNI space')

nv = ipyniivue.Niivue(crosshair_color=[0,1,0,1])
nv.add_volume('./wsub-01_ses-01_7T_T1w_defaced.nii')
nv