Skip to content

FIX: resampling bug #22

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
May 10, 2020
Merged

FIX: resampling bug #22

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
35c902c
FIX: resampling bug
eilidhmacnicol May 6, 2020
a17b557
FIX: explicit in antsAI
eilidhmacnicol May 8, 2020
b637860
FIX: remove compatibility for ants<2.2
eilidhmacnicol May 8, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
87 changes: 46 additions & 41 deletions nirodents/workflows/brainextraction.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,13 +13,13 @@
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from nipype.interfaces.ants import N4BiasFieldCorrection, Atropos
from nipype.interfaces.ants.utils import ResampleImageBySpacing, AI # , ImageMath,
from nipype.interfaces.ants.utils import AI # , ImageMath, ResampleImageBySpacing,
from nipype.interfaces.afni import MaskTool
# from nipype.interfaces.fsl import ApplyMask
from nipype.interfaces.io import DataSink

# niworkflows
from niworkflows.interfaces.ants import ImageMath
from niworkflows.utils.images import resample_by_spacing
from niworkflows.interfaces.nibabel import ApplyMask
from niworkflows.interfaces.fixes import (
FixHeaderRegistration as Registration,
Expand Down Expand Up @@ -70,6 +70,17 @@ def init_rodent_brain_extraction_wf(
tpl_tissue_labels = get_template(in_template,resolution=debug + 1, desc='cerebrum', suffix='dseg')
tpl_brain_mask = get_template(in_template, resolution=debug + 1, desc='cerebrum', suffix='mask')

# resample template and target
res_tmpl = pe.Node(niu.Function(function=res_by_spc,
input_names=['in_file'], output_names=['out_file']), name='res_tmpl')
if tpl_target_path:
res_tmpl.inputs.in_file = tpl_target_path

res_target = pe.Node(niu.Function(function=res_by_spc,
input_names=['in_file'], output_names=['out_file']), name='res_target')
res_target2 = pe.Node(niu.Function(function=res_by_spc,
input_names=['in_file'], output_names=['out_file']), name='res_target2')

dil_mask = pe.Node(MaskTool(), name = 'dil_mask')
dil_mask.inputs.outputtype = 'NIFTI_GZ'
dil_mask.inputs.dilate_inputs = '2'
Expand All @@ -88,15 +99,6 @@ def init_rodent_brain_extraction_wf(
bspline_fitting_distance=bspline_fitting_distance),
n_procs=omp_nthreads, name='inu_n4', iterfield=['input_image'])

# resample template and target
res_tmpl = pe.Node(ResampleImageBySpacing(
out_spacing=(0.4, 0.4, 0.4), apply_smoothing=False), name='res_tmpl')
if tpl_target_path:
res_tmpl.inputs.input_image = str(tpl_target_path)
res_target = pe.Node(ResampleImageBySpacing(
out_spacing=(0.4, 0.4, 0.4), apply_smoothing=False), name='res_target')
res_target2 = pe.Node(ResampleImageBySpacing(
out_spacing=(0.4, 0.4, 0.4), apply_smoothing=False), name='res_target2')

lap_tmpl = pe.Node(ImageMath(operation='Laplacian', op2='0.4'), name='lap_tmpl')
if tpl_target_path:
Expand All @@ -118,6 +120,7 @@ def init_rodent_brain_extraction_wf(
search_factor=(2, 0.015),
principal_axes=False,
convergence=(10, 1e-6, 10),
search_grid=(1, (1, 2, 2)),
verbose=True),
name='init_aff',
n_procs=omp_nthreads)
Expand All @@ -126,16 +129,8 @@ def init_rodent_brain_extraction_wf(
warp_mask = pe.Node(ApplyTransforms(
interpolation='Linear', invert_transform_flags=True), name='warp_mask')

# Tolerate missing ANTs at construction time
_ants_version = Registration().version
if _ants_version and parseversion(_ants_version) >= Version('2.3.0'):
init_aff.inputs.search_grid = (1, (1, 2, 2))

fixed_mask_trait = 'fixed_image_mask'
moving_mask_trait = 'moving_image_mask'
if _ants_version and parseversion(_ants_version) >= Version('2.2.0'):
fixed_mask_trait += 's'
moving_mask_trait += 's'
fixed_mask_trait = 'fixed_image_masks'
moving_mask_trait = 'moving_image_masks'

# Set up initial spatial normalization
init_settings_file = f'data/brainextraction_{init_normalization_quality}_{modality}.json'
Expand All @@ -150,16 +145,10 @@ def init_rodent_brain_extraction_wf(
inu_n4_final = pe.MapNode(
N4BiasFieldCorrection(
dimension=3, save_bias=True, copy_header=True,
n_iterations=[50] * 5, convergence_threshold=1e-7, shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance),
n_iterations=[50] * 5, convergence_threshold=1e-7,
bspline_fitting_distance=bspline_fitting_distance,
rescale_intensities = True, shrink_factor=4),
n_procs=omp_nthreads, name='inu_n4_final', iterfield=['input_image'])
if _ants_version and parseversion(_ants_version) >= Version('2.1.0'):
inu_n4_final.inputs.rescale_intensities = True
else:
warn("""\
Found ANTs version %s, which is too old. Please consider upgrading to 2.1.0 or \
greater so that the --rescale-intensities option is available with \
N4BiasFieldCorrection.""" % _ants_version, DeprecationWarning)

split_init_transforms = pe.Node(niu.Split(splits=[1,1]), name='split_init_transforms')
mrg_init_transforms = pe.Node(niu.Merge(2), name='mrg_init_transforms')
Expand Down Expand Up @@ -217,16 +206,16 @@ def init_rodent_brain_extraction_wf(
wf.connect([
# resampling, truncation, initial N4, and creation of laplacian
(inputnode, trunc, [('in_files', 'op1')]),
(trunc, res_target, [(('output_image', _pop), 'input_image')]),
(res_target, inu_n4, [('output_image', 'input_image')]),
(trunc, res_target, [(('output_image', _pop), 'in_file')]),
(res_target, inu_n4, [('out_file', 'input_image')]),

# dilation of input mask
(inputnode, dil_mask, [('in_mask', 'in_file')]),

# ants AI inputs
(inu_n4, init_aff, [(('output_image', _pop), 'moving_image')]),
(dil_mask, init_aff, [('out_file', 'fixed_image_mask')]),
(res_tmpl, init_aff, [('output_image', 'fixed_image')]),
(res_tmpl, init_aff, [('out_file', 'fixed_image')]),

# warp mask to individual space
(dil_mask, warp_mask, [('out_file', 'input_image')]),
Expand All @@ -241,10 +230,10 @@ def init_rodent_brain_extraction_wf(
(inu_n4_final, lap_target, [(('output_image', _pop), 'op1')]),
(lap_target, norm_lap_target, [('output_image', 'op1')]),
(norm_lap_target, mrg_target, [('output_image', 'in2')]),
(inu_n4_final, res_target2, [(('output_image', _pop), 'input_image')]),
(res_target2, mrg_target, [('output_image', 'in1')]),
(inu_n4_final, res_target2, [(('output_image', _pop), 'in_file')]),
(res_target2, mrg_target, [('out_file', 'in1')]),

(res_tmpl, mrg_tmpl, [('output_image', 'in1')]),
(res_tmpl, mrg_tmpl, [('out_file', 'in1')]),
(lap_tmpl, norm_lap_tmpl, [('output_image', 'op1')]),
(norm_lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),

Expand Down Expand Up @@ -295,22 +284,22 @@ def init_rodent_brain_extraction_wf(
elif modality == 'mp2rage':
wf.connect([
# resampling and creation of laplacians
(inputnode, res_target, [('in_files', 'input_image')]),
(inputnode, res_target, [('in_files', 'in_file')]),
(inputnode, lap_target, [('in_files', 'op1')]),
(lap_target, norm_lap_target, [('output_image', 'op1')]),
(norm_lap_target, mrg_target, [('output_image', 'in2')]),
(res_target, mrg_target, [('output_image', 'in1')]),
(res_target, mrg_target, [('out_file', 'in1')]),

(res_tmpl, mrg_tmpl, [('output_image', 'in1')]),
(res_tmpl, mrg_tmpl, [('out_file', 'in1')]),
(lap_tmpl, norm_lap_tmpl, [('output_image', 'op1')]),
(norm_lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),

#dilation of input mask
(inputnode, dil_mask, [('in_mask', 'in_file')]),

# ants AI inputs
(res_tmpl, init_aff, [('output_image', 'fixed_image')]),
(res_target, init_aff, [('output_image', 'moving_image')]),
(res_tmpl, init_aff, [('out_file', 'fixed_image')]),
(res_target, init_aff, [('out_file', 'moving_image')]),
(dil_mask, init_aff, [('out_file', 'fixed_image_mask')]),

# warp mask to individual space
Expand Down Expand Up @@ -365,3 +354,19 @@ def _pop(in_files):
if isinstance(in_files, (list, tuple)):
return in_files[0]
return in_files

def res_by_spc(in_file, out_file = None):
import os.path as op
from niworkflows.utils.images import resample_by_spacing
import nibabel as nib
resampled = resample_by_spacing(in_file, (0.4, 0.4, 0.4), clip = False)

if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == '.gz':
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath('{}_resampled{}'.format(fname, ext))

nib.save(resampled, out_file)
return out_file