def detect_c2c3(nii_im, nii_seg, contrast, nb_sag_avg=7.0, verbose=1):
"""
Detect the posterior edge of C2-C3 disc.
:param nii_im:
:param nii_seg:
:param contrast:
:param verbose:
:return:
"""
# path to the pmj detector
path_model = os.path.join(sct.__data_dir__, 'c2c3_disc_models',
'{}_model'.format(contrast))
orientation_init = nii_im.orientation
z_seg_max = np.max(np.where(nii_seg.change_orientation('PIR').data)[1])
# Flatten sagittal
nii_im = flatten_sagittal(nii_im, nii_seg, verbose=verbose)
nii_seg_flat = flatten_sagittal(nii_seg, nii_seg, verbose=verbose)
# create temporary folder with intermediate results
sct.log.info("Creating temporary folder...")
tmp_folder = sct.TempFolder()
tmp_folder.chdir()
# Extract mid-slice
nii_im.change_orientation('PIR')
nii_seg_flat.change_orientation('PIR')
mid_RL = int(np.rint(nii_im.dim[2] * 1.0 / 2))
nb_sag_avg_half = int(nb_sag_avg / 2 / nii_im.dim[6])
midSlice = np.mean(nii_im.data[:, :, mid_RL - nb_sag_avg_half:mid_RL +
nb_sag_avg_half + 1], 2) # average 7 slices
midSlice_seg = nii_seg_flat.data[:, :, mid_RL]
nii_midSlice = msct_image.zeros_like(nii_im)
nii_midSlice.data = midSlice
nii_midSlice.save('data_midSlice.nii')
# Run detection
sct.printv('Run C2-C3 detector...', verbose)
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_detection = 'isct_spine_detect -ctype=dpdt "%s" "%s" "%s"' % \
(path_model, 'data_midSlice', 'data_midSlice_pred')
sct.run(cmd_detection, verbose=0, raise_exception=False)
pred = nib.load('data_midSlice_pred_svm.hdr').get_data()
if verbose >= 2:
# copy the "prediction data before post-processing" in an Image object
nii_pred_before_postPro = nii_midSlice.copy()
nii_pred_before_postPro.data = pred # 2D data with orientation, mid sag slice of the original data
nii_pred_before_postPro.save(
"pred_midSlice_before_postPro.nii.gz") # save it)
# Create mask along centerline
midSlice_mask = np.zeros(midSlice_seg.shape)
mask_halfSize = int(np.rint(25.0 / nii_midSlice.dim[4]))
for z in range(midSlice_mask.shape[1]):
row = midSlice_seg[:,
z] # 2D data with PI orientation, mid sag slice of the original data
if np.any(row > 0):
med_y = int(np.rint(np.median(np.where(row > 0))))
midSlice_mask[
med_y - mask_halfSize:med_y + mask_halfSize,
z] = 1 # 2D data with PI orientation, mid sag slice of the original data
if verbose >= 2:
# copy the created mask in an Image object
nii_postPro_mask = nii_midSlice.copy()
nii_postPro_mask.data = midSlice_mask # 2D data with PI orientation, mid sag slice of the original data
nii_postPro_mask.save("mask_midSlice.nii.gz") # save it
# mask prediction
pred[midSlice_mask == 0] = 0
pred[:, z_seg_max:] = 0 # Mask above SC segmentation
if verbose >= 2:
# copy the "prediction data after post-processing" in an Image object
nii_pred_after_postPro = nii_midSlice.copy()
nii_pred_after_postPro.data = pred
nii_pred_after_postPro.save(
"pred_midSlice_after_postPro.nii.gz") # save it
# assign label to voxel
nii_c2c3 = zeros_like(nii_seg_flat) # 3D data with PIR orientaion
if np.any(pred > 0):
sct.printv('C2-C3 detected...', verbose)
pred_bin = (pred > 0).astype(np.int_)
coord_max = np.where(pred == np.max(pred))
pa_c2c3, is_c2c3 = coord_max[0][0], coord_max[1][0]
nii_seg.change_orientation('PIR')
rl_c2c3 = int(
np.rint(center_of_mass(np.array(nii_seg.data[:, is_c2c3, :]))[1]))
nii_c2c3.data[pa_c2c3, is_c2c3, rl_c2c3] = 3
else:
sct.printv('C2-C3 not detected...', verbose)
# remove temporary files
tmp_folder.chdir_undo()
if verbose < 2:
sct.log.info("Remove temporary files...")
tmp_folder.cleanup()
nii_c2c3.change_orientation(orientation_init)
return nii_c2c3
def detect_c2c3(nii_im, nii_seg, contrast, nb_sag_avg=7.0, verbose=1):
"""
Detect the posterior edge of C2-C3 disc.
:param nii_im:
:param nii_seg:
:param contrast:
:param verbose:
:return:
"""
# path to the pmj detector
path_model = os.path.join(sct.__data_dir__, 'c2c3_disc_models',
'{}_model'.format(contrast))
# check if model exists
if not os.path.isfile(path_model + '.yml'):
raise FileNotFoundError(
"The model file {} does not exist. Please download it using sct_download_data"
.format(path_model + '.yml'))
orientation_init = nii_im.orientation
z_seg_max = np.max(np.where(nii_seg.change_orientation('PIR').data)[1])
# Flatten sagittal
nii_im = flatten_sagittal(nii_im, nii_seg, verbose=verbose)
nii_seg_flat = flatten_sagittal(nii_seg, nii_seg, verbose=verbose)
# create temporary folder with intermediate results
logger.info("Creating temporary folder...")
tmp_folder = sct.TempFolder()
tmp_folder.chdir()
# Extract mid-slice
nii_im.change_orientation('PIR')
nii_seg_flat.change_orientation('PIR')
mid_RL = int(np.rint(nii_im.dim[2] * 1.0 / 2))
nb_sag_avg_half = int(nb_sag_avg / 2 / nii_im.dim[6])
midSlice = np.mean(nii_im.data[:, :, mid_RL - nb_sag_avg_half:mid_RL +
nb_sag_avg_half + 1], 2) # average 7 slices
midSlice_seg = nii_seg_flat.data[:, :, mid_RL]
nii_midSlice = zeros_like(nii_im)
nii_midSlice.data = midSlice
nii_midSlice.save('data_midSlice.nii')
# Run detection
logger.info('Run C2-C3 detector...')
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_detection = 'isct_spine_detect -ctype=dpdt "%s" "%s" "%s"' % \
(path_model, 'data_midSlice', 'data_midSlice_pred')
# The command below will fail, but we don't care because it will output an image (prediction), which we
# will use later on.
s, o = run_proc(cmd_detection,
verbose=0,
is_sct_binary=True,
raise_exception=False)
pred = nib.load('data_midSlice_pred_svm.hdr').get_data()
if verbose >= 2:
# copy the "prediction data before post-processing" in an Image object
nii_pred_before_postPro = nii_midSlice.copy()
nii_pred_before_postPro.data = pred # 2D data with orientation, mid sag slice of the original data
nii_pred_before_postPro.save(
"pred_midSlice_before_postPro.nii.gz") # save it)
# DEBUG trick: check if the detection succeed by running: fsleyes data_midSlice data_midSlice_pred_svm -cm red -dr 0 100
# If a "red cluster" is observed in the neighbourhood of C2C3, then the model detected it.
# Create mask along centerline
midSlice_mask = np.zeros(midSlice_seg.shape)
mask_halfSize = int(np.rint(25.0 / nii_midSlice.dim[4]))
for z in range(midSlice_mask.shape[1]):
row = midSlice_seg[:,
z] # 2D data with PI orientation, mid sag slice of the original data
if np.any(row > 0):
med_y = int(np.rint(np.median(np.where(row > 0))))
midSlice_mask[
med_y - mask_halfSize:med_y + mask_halfSize,
z] = 1 # 2D data with PI orientation, mid sag slice of the original data
if verbose >= 2:
# copy the created mask in an Image object
nii_postPro_mask = nii_midSlice.copy()
nii_postPro_mask.data = midSlice_mask # 2D data with PI orientation, mid sag slice of the original data
nii_postPro_mask.save("mask_midSlice.nii.gz") # save it
# mask prediction
pred[midSlice_mask == 0] = 0
pred[:, z_seg_max:] = 0 # Mask above SC segmentation
if verbose >= 2:
# copy the "prediction data after post-processing" in an Image object
nii_pred_after_postPro = nii_midSlice.copy()
nii_pred_after_postPro.data = pred
nii_pred_after_postPro.save(
"pred_midSlice_after_postPro.nii.gz") # save it
# assign label to voxel
nii_c2c3 = zeros_like(nii_seg_flat) # 3D data with PIR orientaion
if np.any(pred > 0):
logger.info('C2-C3 detected...')
pred_bin = (pred > 0).astype(np.int_)
coord_max = np.where(pred == np.max(pred))
pa_c2c3, is_c2c3 = coord_max[0][0], coord_max[1][0]
nii_seg.change_orientation('PIR')
rl_c2c3 = int(
np.rint(center_of_mass(np.array(nii_seg.data[:, is_c2c3, :]))[1]))
nii_c2c3.data[pa_c2c3, is_c2c3, rl_c2c3] = 3
else:
logger.warning('C2-C3 not detected...')
# remove temporary files
tmp_folder.chdir_undo()
if verbose < 2:
logger.info("Remove temporary files...")
tmp_folder.cleanup()
else:
logger.info("Temporary files saved to " + tmp_folder.get_path())
nii_c2c3.change_orientation(orientation_init)
return nii_c2c3
def detect_c2c3(nii_im, nii_seg, contrast, verbose=1):
"""
Detect the posterior edge of C2-C3 disc.
:param nii_im:
:param nii_seg:
:param contrast:
:param verbose:
:return:
"""
# path to the pmj detector
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
path_model = os.path.join(path_sct, 'data', 'c2c3_disc_models',
'{}_model'.format(contrast))
orientation_init = nii_im.orientation
# Flatten sagittal
nii_im = flatten_sagittal(nii_im,
nii_seg,
centerline_fitting='hanning',
verbose=verbose)
nii_seg_flat = flatten_sagittal(nii_seg,
nii_seg,
centerline_fitting='hanning',
verbose=verbose)
# create temporary folder with intermediate results
sct.log.info("Creating temporary folder...")
tmp_folder = sct.TempFolder()
tmp_folder.chdir()
# Extract mid-slice
nii_im.change_orientation('PIR')
nii_seg_flat.change_orientation('PIR')
mid_RL = int(np.rint(nii_im.dim[2] * 1.0 / 2))
midSlice = nii_im.data[:, :, mid_RL]
midSlice_seg = nii_seg_flat.data[:, :, mid_RL]
nii_midSlice = msct_image.zeros_like(nii_im)
nii_midSlice.data = midSlice
nii_midSlice.save('data_midSlice.nii')
# Run detection
sct.printv('Run C2-C3 detector...', verbose)
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_detection = 'isct_spine_detect -ctype=dpdt "%s" "%s" "%s"' % \
(path_model, 'data_midSlice', 'data_midSlice_pred')
sct.run(cmd_detection, verbose=0, raise_exception=False)
# sct.run(cmd_detection, verbose=0)
pred = nib.load('data_midSlice_pred_svm.hdr').get_data()
# Create mask along centerline
midSlice_mask = np.zeros(midSlice_seg.shape)
mask_halfSize = 25
for z in range(midSlice_mask.shape[1]):
row = midSlice_seg[:, z]
if np.any(row):
med_y = int(np.rint(np.median(np.where(row))))
midSlice_mask[med_y - mask_halfSize:med_y + mask_halfSize] = 1
# mask prediction
pred[midSlice_mask == 0] = 0
# assign label to voxel
nii_c2c3 = zeros_like(nii_seg_flat)
if np.any(pred > 0):
sct.printv('C2-C3 detected...', verbose)
coord_max = np.where(pred == np.max(pred))
pa_c2c3, is_c2c3 = coord_max[0][0], coord_max[1][0]
nii_seg.change_orientation('PIR')
rl_c2c3 = int(np.rint(center_of_mass(nii_seg.data[:, is_c2c3, :])[1]))
nii_c2c3.data[pa_c2c3, is_c2c3, rl_c2c3] = 3
else:
sct.printv('C2-C3 not detected...', verbose)
# remove temporary files
tmp_folder.chdir_undo()
sct.log.info("Remove temporary files...")
tmp_folder.cleanup()
nii_c2c3.change_orientation(orientation_init)
return nii_c2c3
def detect_c2c3(nii_im, nii_seg, contrast, nb_sag_avg=7.0, verbose=1):
"""
Detect the posterior edge of C2-C3 disc.
:param nii_im:
:param nii_seg:
:param contrast:
:param verbose:
:return:
"""
# path to the pmj detector
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
path_model = os.path.join(path_sct, 'data', 'c2c3_disc_models',
'{}_model'.format(contrast))
orientation_init = nii_im.orientation
z_seg_max = np.max(np.where(nii_seg.change_orientation('PIR').data)[1])
# Flatten sagittal
nii_im = flatten_sagittal(nii_im,
nii_seg,
centerline_fitting='hanning',
verbose=verbose)
nii_seg_flat = flatten_sagittal(nii_seg,
nii_seg,
centerline_fitting='hanning',
verbose=verbose)
# create temporary folder with intermediate results
sct.log.info("Creating temporary folder...")
tmp_folder = sct.TempFolder()
# print tmp_folder.path_tmp
tmp_folder.chdir()
# Extract mid-slice
nii_im.change_orientation('PIR')
nii_seg_flat.change_orientation('PIR')
mid_RL = int(np.rint(nii_im.dim[2] * 1.0 / 2))
nb_sag_avg_half = int(nb_sag_avg / 2 / nii_im.dim[6])
midSlice = np.mean(nii_im.data[:, :, mid_RL - nb_sag_avg_half:mid_RL +
nb_sag_avg_half + 1], 2) # average 7 slices
midSlice_seg = nii_seg_flat.data[:, :, mid_RL]
nii_midSlice = msct_image.zeros_like(nii_im)
nii_midSlice.data = midSlice
nii_midSlice.save('data_midSlice.nii')
# Run detection
sct.printv('Run C2-C3 detector...', verbose)
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_detection = 'isct_spine_detect -ctype=dpdt "%s" "%s" "%s"' % \
(path_model, 'data_midSlice', 'data_midSlice_pred')
sct.run(cmd_detection, verbose=0, raise_exception=False)
pred = nib.load('data_midSlice_pred_svm.hdr').get_data()
# Create mask along centerline
midSlice_mask = np.zeros(midSlice_seg.shape)
mask_halfSize = int(np.rint(25.0 / nii_midSlice.dim[4]))
for z in range(midSlice_mask.shape[1]):
row = midSlice_seg[:, z]
if np.any(row):
med_y = int(np.rint(np.median(np.where(row))))
midSlice_mask[med_y - mask_halfSize:med_y + mask_halfSize] = 1
# mask prediction
pred[midSlice_mask == 0] = 0
# dist_medulla = 30.0 if contrast == 't1' else 40.0 # Observation: segmentation ends higher on t2 images than t1
# z_seg_max -= int(np.rint(dist_medulla / nii_midSlice.dim[5])) # TODO: take into account the curvature
pred[:, z_seg_max:] = 0 # Mask above SC segmentation
# assign label to voxel
nii_c2c3 = zeros_like(nii_seg_flat)
if np.any(pred > 0):
sct.printv('C2-C3 detected...', verbose)
pred_bin = (pred > 0).astype(np.int_)
# labeled_pred, nb_regions = label_regions(pred_bin, return_num=True)
# if nb_regions > 1: # if there are several clusters of voxels detected
# region_idx_top, region_z_top = 0, 0
# for region_idx in range(1, nb_regions+1):
# pred_idx = (labeled_pred == region_idx).astype(np.int_)
# pa_com, is_com = center_of_mass(pred_idx)
# if is_com >= region_z_top:
# region_idx_top = region_idx
# region_z_top = is_com
# pred[labeled_pred != region_idx_top] = 0 # then keep the one located at the top (IS direction)
coord_max = np.where(pred == np.max(pred))
pa_c2c3, is_c2c3 = coord_max[0][0], coord_max[1][0]
nii_seg.change_orientation('PIR')
rl_c2c3 = int(np.rint(center_of_mass(nii_seg.data[:, is_c2c3, :])[1]))
nii_c2c3.data[pa_c2c3, is_c2c3, rl_c2c3] = 3
else:
sct.printv('C2-C3 not detected...', verbose)
# remove temporary files
tmp_folder.chdir_undo()
sct.log.info("Remove temporary files...")
tmp_folder.cleanup()
nii_c2c3.change_orientation(orientation_init)
return nii_c2c3
def detect_c2c3(nii_im, nii_seg, contrast, nb_sag_avg=7.0, verbose=1):
"""
Detect the posterior edge of C2-C3 disc.
:param nii_im:
:param nii_seg:
:param contrast:
:param verbose:
:return:
"""
# path to the pmj detector
path_model = os.path.join(sct.__data_dir__, 'c2c3_disc_models', '{}_model'.format(contrast))
orientation_init = nii_im.orientation
z_seg_max = np.max(np.where(nii_seg.change_orientation('PIR').data)[1])
# Flatten sagittal
nii_im = flatten_sagittal(nii_im, nii_seg,verbose=verbose)
nii_seg_flat = flatten_sagittal(nii_seg, nii_seg, verbose=verbose)
# create temporary folder with intermediate results
logger.info("Creating temporary folder...")
tmp_folder = sct.TempFolder()
tmp_folder.chdir()
# Extract mid-slice
nii_im.change_orientation('PIR')
nii_seg_flat.change_orientation('PIR')
mid_RL = int(np.rint(nii_im.dim[2] * 1.0 / 2))
nb_sag_avg_half = int(nb_sag_avg / 2 / nii_im.dim[6])
midSlice = np.mean(nii_im.data[:, :, mid_RL-nb_sag_avg_half:mid_RL+nb_sag_avg_half+1], 2) # average 7 slices
midSlice_seg = nii_seg_flat.data[:, :, mid_RL]
nii_midSlice = msct_image.zeros_like(nii_im)
nii_midSlice.data = midSlice
nii_midSlice.save('data_midSlice.nii')
# Run detection
logger.info('Run C2-C3 detector...')
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_detection = 'isct_spine_detect -ctype=dpdt "%s" "%s" "%s"' % \
(path_model, 'data_midSlice', 'data_midSlice_pred')
# The command below will fail, but we don't care because it will output an image (prediction), which we
# will use later on.
s, o = sct.run(cmd_detection, verbose=0, is_sct_binary=True, raise_exception=False)
pred = nib.load('data_midSlice_pred_svm.hdr').get_data()
if verbose >= 2:
# copy the "prediction data before post-processing" in an Image object
nii_pred_before_postPro = nii_midSlice.copy()
nii_pred_before_postPro.data = pred # 2D data with orientation, mid sag slice of the original data
nii_pred_before_postPro.save("pred_midSlice_before_postPro.nii.gz") # save it)
# Create mask along centerline
midSlice_mask = np.zeros(midSlice_seg.shape)
mask_halfSize = int(np.rint(25.0 / nii_midSlice.dim[4]))
for z in range(midSlice_mask.shape[1]):
row = midSlice_seg[:, z] # 2D data with PI orientation, mid sag slice of the original data
if np.any(row > 0):
med_y = int(np.rint(np.median(np.where(row > 0))))
midSlice_mask[med_y-mask_halfSize:med_y+mask_halfSize, z] = 1 # 2D data with PI orientation, mid sag slice of the original data
if verbose >= 2:
# copy the created mask in an Image object
nii_postPro_mask = nii_midSlice.copy()
nii_postPro_mask.data = midSlice_mask # 2D data with PI orientation, mid sag slice of the original data
nii_postPro_mask.save("mask_midSlice.nii.gz") # save it
# mask prediction
pred[midSlice_mask == 0] = 0
pred[:, z_seg_max:] = 0 # Mask above SC segmentation
if verbose >= 2:
# copy the "prediction data after post-processing" in an Image object
nii_pred_after_postPro = nii_midSlice.copy()
nii_pred_after_postPro.data = pred
nii_pred_after_postPro.save("pred_midSlice_after_postPro.nii.gz") # save it
# assign label to voxel
nii_c2c3 = zeros_like(nii_seg_flat) # 3D data with PIR orientaion
if np.any(pred > 0):
logger.info('C2-C3 detected...')
pred_bin = (pred > 0).astype(np.int_)
coord_max = np.where(pred == np.max(pred))
pa_c2c3, is_c2c3 = coord_max[0][0], coord_max[1][0]
nii_seg.change_orientation('PIR')
rl_c2c3 = int(np.rint(center_of_mass(np.array(nii_seg.data[:, is_c2c3, :]))[1]))
nii_c2c3.data[pa_c2c3, is_c2c3, rl_c2c3] = 3
else:
logger.warning('C2-C3 not detected...')
# remove temporary files
tmp_folder.chdir_undo()
if verbose < 2:
logger.info("Remove temporary files...")
tmp_folder.cleanup()
nii_c2c3.change_orientation(orientation_init)
return nii_c2c3