def generate_qc(fn_in, fn_seg, args, path_qc):
"""Generate a QC entry allowing to quickly review the segmentation process."""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
from spinalcordtoolbox.resample.nipy_resample import resample_file
# Resample to fixed resolution (see #2063)
tmp_folder = sct.TempFolder()
fn_in_r = os.path.join(tmp_folder.path_tmp, 'img_r.nii.gz')
# Orient to RPI and retrieve pixel size in IS direction (z)
im_fn = Image(fn_in).change_orientation('RPI').save(fn_in_r)
resample_file(fn_in_r, fn_in_r, '0.5x0.5x' + str(im_fn.dim[6]), 'mm', 'nn',
0)
fn_seg_r = os.path.join(tmp_folder.path_tmp, 'seg_r.nii.gz')
Image(fn_seg).change_orientation('RPI').save(fn_seg_r)
resample_file(fn_seg_r, fn_seg_r, '0.5x0.5x' + str(im_fn.dim[6]), 'mm',
'nn', 0)
# TODO: investigate the following issue further (Julien 2018-12-01):
# fn_in_r and fn_seg_r should be in nii.gz format, otherwise qcslice.Axial outputs an memmap instead of
# an array.
qc.add_entry(
src=fn_in,
process="sct_deepseg_sc",
args=args,
path_qc=path_qc,
plane='Axial',
qcslice=qcslice.Axial([Image(fn_in_r), Image(fn_seg_r)]),
qcslice_operations=[qc.QcImage.listed_seg],
qcslice_layout=lambda x: x.mosaic(),
)
def generate_qc(fn_in, fn_seg, args, path_qc):
"""
Generate a QC entry allowing to quickly review the segmentation process.
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
from spinalcordtoolbox.resample.nipy_resample import resample_file
# Resample to fixed resolution (see #2063)
tmp_folder = sct.TempFolder()
fn_in_r = os.path.join(tmp_folder.path_tmp, 'img_r.nii.gz')
# Orient to RPI and retrieve pixel size in IS direction (z)
im_fn = Image(fn_in).change_orientation('RPI').save(fn_in_r)
resample_file(fn_in_r, fn_in_r, '0.5x0.5x' + str(im_fn.dim[6]), 'mm', 'nn',
0)
fn_seg_r = os.path.join(tmp_folder.path_tmp, 'seg_r.nii.gz')
Image(fn_seg).change_orientation('RPI').save(fn_seg_r)
resample_file(fn_seg_r, fn_seg_r, '0.5x0.5x' + str(im_fn.dim[6]), 'mm',
'nn', 0)
qc.add_entry(
src=fn_in,
process="sct_propseg",
args=args,
path_qc=path_qc,
plane='Axial',
qcslice=qcslice.Axial([Image(fn_in_r), Image(fn_seg_r)]),
qcslice_operations=[qc.QcImage.listed_seg],
qcslice_layout=lambda x: x.mosaic(),
)
def generate_qc(fn_in, fn_labeled, args, path_qc):
"""
Generate a quick visualization of vertebral labeling
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
def label_vertebrae(self, mask):
"""
Draw vertebrae areas, then add text showing the vertebrae names.
"""
import matplotlib.pyplot as plt
import scipy.ndimage
self.listed_seg(mask)
ax = plt.gca()
a = [0.0]
data = mask
for index, val in np.ndenumerate(data):
if val not in a:
a.append(val)
index = int(val)
if index in self._labels_regions.values():
color = self._labels_color[index]
y, x = scipy.ndimage.measurements.center_of_mass(
np.where(data == val, data, 0))
# Draw text with a shadow
x += 10
label = list(self._labels_regions.keys())[list(
self._labels_regions.values()).index(index)]
ax.text(x, y, label, color='black', clip_on=True)
x -= 0.5
y -= 0.5
ax.text(x, y, label, color=color, clip_on=True)
qc.add_entry(
src=fn_in,
process='sct_label_vertebrae',
args=args,
path_qc=path_qc,
plane='Sagittal',
dpi=100,
qcslice=qcslice.Sagittal([Image(fn_in),
Image(fn_labeled)]),
qcslice_operations=[label_vertebrae],
qcslice_layout=lambda x: x.single(),
)
def generate_qc(fn_in, fn_seg, args, path_qc):
"""Generate a QC entry allowing to quickly review the segmentation process."""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
qc.add_entry(
src=fn_in,
process="sct_deepseg_sc",
args=args,
path_qc=path_qc,
plane='Axial',
qcslice=qcslice.Axial([Image(fn_in), Image(fn_seg)]),
qcslice_operations=[qc.QcImage.listed_seg],
qcslice_layout=lambda x: x.mosaic(),
)
def generate_qc(fn_in, fn_wm, args, path_qc):
"""
Generate a QC entry allowing to quickly review the warped template.
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
qc.add_entry(
src=fn_in,
process="sct_warp_template",
args=args,
path_qc=path_qc,
plane='Axial',
qcslice=qcslice.Axial([Image(fn_in), Image(fn_wm)]),
qcslice_operations=[qc.QcImage.template],
qcslice_layout=lambda x: x.mosaic(),
)
def generate_qc(fname_in, fname_out, args, path_qc):
"""
Generate a QC entry allowing to quickly review the PMJ position
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
def highlight_pmj(self, mask):
"""
Hook to show a rectangle where PMJ is on the slice
"""
import matplotlib.pyplot as plt
import matplotlib.patches as patches
y, x = np.where(mask == 50)
ax = plt.gca()
img = np.full_like(mask, np.nan)
ax.imshow(img, cmap='gray', alpha=0)
rect = patches.Rectangle((x - 10, y - 10),
20,
20,
linewidth=2,
edgecolor='lime',
facecolor='none')
ax.add_patch(rect)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
qc.add_entry(
src=fname_in,
process="sct_detect_pmj",
args=args,
path_qc=path_qc,
plane="Sagittal",
qcslice=qcslice.Sagittal([Image(fname_in),
Image(fname_out)]),
qcslice_operations=[highlight_pmj],
qcslice_layout=lambda x: x.single(),
)
def generate_qc(fn_input, fn_centerline, fn_output, args, path_qc):
"""
Generate a QC entry allowing to quickly review the straightening process.
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
# Just display the straightened spinal cord
img_out = Image(fn_output)
foreground = qcslice.Sagittal([img_out]).single()[0]
qc.add_entry(
src=fn_input,
process="sct_straighten_spinalcord",
args=args,
path_qc=path_qc,
plane="Sagittal",
foreground=foreground,
)
def generate_qc(fname_data, fname_template2anat, fname_seg, args, path_qc):
"""
Generate a QC entry allowing to quickly review the straightening process.
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
qc.add_entry(
src=fname_data,
process="sct_register_to_template",
args=args,
path_qc=path_qc,
plane="Axial",
qcslice=qcslice.Axial(
[Image(fname_data),
Image(fname_template2anat),
Image(fname_seg)]),
qcslice_operations=[qc.QcImage.no_seg_seg],
qcslice_layout=lambda x: x.mosaic()[:2],
)
def generate_qc(fname_in, fname_gm, fname_wm, param_seg, args, path_qc):
"""
Generate a QC entry allowing to quickly review the segmentation process.
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
im_org = Image(fname_in)
im_gm = Image(fname_gm)
im_wm = Image(fname_wm)
# create simple compound segmentation image for QC purposes
if param_seg.type_seg == 'bin':
im_wm.data[im_wm.data == 1] = 1
im_gm.data[im_gm.data == 1] = 2
else:
# binarize anyway
im_wm.data[im_wm.data >= param_seg.thr_bin] = 1
im_wm.data[im_wm.data < param_seg.thr_bin] = 0
im_gm.data[im_gm.data >= param_seg.thr_bin] = 2
im_gm.data[im_gm.data < param_seg.thr_bin] = 0
im_seg = im_gm
im_seg.data += im_wm.data
s = qcslice.Axial([im_org, im_seg])
qc.add_entry(
src=fname_in,
process="sct_segment_graymatter",
args=args,
path_qc=path_qc,
plane='Axial',
qcslice=s,
qcslice_operations=[qc.QcImage.listed_seg],
qcslice_layout=lambda x: x.mosaic(),
)
def generate_qc(fname_in, fname_gm, fname_wm, param_seg, args, path_qc):
"""
Generate a QC entry allowing to quickly review the segmentation process.
"""
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
im_org = Image(fname_in)
im_gm = Image(fname_gm)
im_wm = Image(fname_wm)
# create simple compound segmentation image for QC purposes
if param_seg.type_seg == 'bin':
im_wm.data[im_wm.data == 1] = 1
im_gm.data[im_gm.data == 1] = 2
else:
# binarize anyway
im_wm.data[im_wm.data >= param_seg.thr_bin] = 1
im_wm.data[im_wm.data < param_seg.thr_bin] = 0
im_gm.data[im_gm.data >= param_seg.thr_bin] = 2
im_gm.data[im_gm.data < param_seg.thr_bin] = 0
im_seg = im_gm
im_seg.data += im_wm.data
s = qcslice.Axial([im_org, im_seg])
qc.add_entry(
src=fname_in,
process="sct_segment_graymatter",
args=args,
path_qc=path_qc,
plane='Axial',
qcslice=s,
qcslice_operations=[qc.QcImage.listed_seg],
qcslice_layout=lambda x: x.mosaic(),
)
