def vertebral_detection(fname, fname_seg, contrast, param, init_disc=[], verbose=1, path_template='', initc2='auto', path_output='../'):
"""
Find intervertebral discs in straightened image using template matching
:param fname:
:param fname_seg:
:param contrast:
:param param: advanced parameters
:param init_disc:
:param verbose:
:param path_template:
:param path_output: output path for verbose=2 pictures
:return:
"""
printv('\nLook for template...', verbose)
# if path_template == '':
# # get path of SCT
# from os import path
# path_script = path.dirname(__file__)
# path_sct = slash_at_the_end(path.dirname(path_script), 1)
# folder_template = 'data/template/'
# path_template = path_sct+folder_template
printv('Path template: '+path_template, verbose)
# adjust file names if MNI-Poly-AMU template is used
fname_level = get_file_label(path_template+'template/', 'vertebral', output='filewithpath')
fname_template = get_file_label(path_template+'template/', contrast.upper()+'-weighted', output='filewithpath')
# if not len(glob(path_template+'MNI-Poly-AMU*.*')) == 0:
# contrast = contrast.upper()
# file_level = '*_level.nii.gz'
# else:
# file_level = '*_levels.nii.gz'
#
# # retrieve file_template based on contrast
# try:
# fname_template_list = glob(path_template + '*' + contrast + '.nii.gz')
# fname_template = fname_template_list[0]
# except IndexError:
# printv('\nERROR: No template found. Please check the provided path.', 1, 'error')
# retrieve disc level from template
# try:
# fname_level_list = glob(path_template+file_level)
# fname_level = fname_level_list[0]
# except IndexError:
# printv('\nERROR: File *_levels.nii.gz not found.', 1, 'error')
# Open template and vertebral levels
printv('\nOpen template and vertebral levels...', verbose)
data_template = Image(fname_template).data
data_disc_template = Image(fname_level).data
# open anatomical volume
im_input = Image(fname)
data = im_input.data
# smooth data
from scipy.ndimage.filters import gaussian_filter
data = gaussian_filter(data, param.smooth_factor, output=None, mode="reflect")
# get dimension of src
nx, ny, nz = data.shape
# define xc and yc (centered in the field of view)
xc = int(round(nx/2)) # direction RL
yc = int(round(ny/2)) # direction AP
# get dimension of template
nxt, nyt, nzt = data_template.shape
# define xc and yc (centered in the field of view)
xct = int(round(nxt/2)) # direction RL
yct = int(round(nyt/2)) # direction AP
# define mean distance (in voxel) between adjacent discs: [C1/C2 -> C2/C3], [C2/C3 -> C4/C5], ..., [L1/L2 -> L2/L3]
centerline_level = data_disc_template[xct, yct, :]
# attribute value to each disc. Starts from max level, then decrease.
min_level = centerline_level[centerline_level.nonzero()].min()
max_level = centerline_level[centerline_level.nonzero()].max()
list_disc_value_template = range(min_level, max_level)
# add disc above top one
list_disc_value_template.insert(int(0), min_level - 1)
printv('\nDisc values from template: ' + str(list_disc_value_template), verbose)
# get diff to find transitions (i.e., discs)
diff_centerline_level = np.diff(centerline_level)
# get disc z-values
list_disc_z_template = diff_centerline_level.nonzero()[0].tolist()
list_disc_z_template.reverse()
printv('Z-values for each disc: ' + str(list_disc_z_template), verbose)
list_distance_template = (
np.diff(list_disc_z_template) * (-1)).tolist() # multiplies by -1 to get positive distances
printv('Distances between discs (in voxel): ' + str(list_distance_template), verbose)
# if automatic mode, find C2/C3 disc
if init_disc == [] and initc2 == 'auto':
printv('\nDetect C2/C3 disk...', verbose)
zrange = range(0, nz)
ind_c2 = list_disc_value_template.index(2)
z_peak = compute_corr_3d(src=data, target=data_template, x=xc, xshift=0, xsize=param.size_RL_initc2, y=yc, yshift=param.shift_AP_initc2, ysize=param.size_AP_initc2, z=0, zshift=param.shift_IS_initc2, zsize=param.size_IS_initc2, xtarget=xct, ytarget=yct, ztarget=list_disc_z_template[ind_c2], zrange=zrange, verbose=verbose, save_suffix='_initC2', gaussian_weighting=True, path_output=path_output)
init_disc = [z_peak, 2]
# if manual mode, open viewer for user to click on C2/C3 disc
if init_disc == [] and initc2 == 'manual':
from sct_viewer import ClickViewer
# reorient image to SAL to be compatible with viewer
im_input_SAL = im_input.copy()
im_input_SAL.change_orientation('SAL')
viewer = ClickViewer(im_input_SAL, orientation_subplot=['sag', 'ax'])
viewer.number_of_slices = 1
pz = 1
viewer.gap_inter_slice = int(10 / pz)
viewer.calculate_list_slices()
viewer.help_url = 'https://sourceforge.net/p/spinalcordtoolbox/wiki/sct_label_vertebrae/attachment/label_vertebrae_viewer.png'
# start the viewer that ask the user to enter a few points along the spinal cord
mask_points = viewer.start()
if mask_points:
# create the mask containing either the three-points or centerline mask for initialization
mask_filename = sct.add_suffix(fname, "_mask_viewer")
sct.run("sct_label_utils -i " + fname + " -create " + mask_points + " -o " + mask_filename, verbose=False)
else:
sct.printv('\nERROR: the viewer has been closed before entering all manual points. Please try again.', verbose, type='error')
# assign new init_disc_z value, which corresponds to the first vector of mask_points. Note, we need to substract from nz due to SAL orientation: in the viewer, orientation is S-I while in this code, it is I-S.
init_disc = [nz-int(mask_points.split(',')[0]), 2]
# display init disc
if verbose == 2:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.matshow(np.mean(data[xc-param.size_RL:xc+param.size_RL, :, :], axis=0).transpose(), fignum=50, cmap=plt.cm.gray, clim=[0, 800], origin='lower')
plt.title('Anatomical image')
plt.autoscale(enable=False) # to prevent autoscale of axis when displaying plot
plt.figure(50), plt.scatter(yc + param.shift_AP_visu, init_disc[0], c='yellow', s=50)
plt.text(yc + param.shift_AP_visu + 4, init_disc[0], str(init_disc[1]) + '/' + str(init_disc[1] + 1),
verticalalignment='center', horizontalalignment='left', color='pink', fontsize=15), plt.draw()
# plt.ion() # enables interactive mode
# FIND DISCS
# ===========================================================================
printv('\nDetect intervertebral discs...', verbose)
# assign initial z and disc
current_z = init_disc[0]
current_disc = init_disc[1]
# mean_distance = mean_distance * pz
# mean_distance_real = np.zeros(len(mean_distance))
# create list for z and disc
list_disc_z = []
list_disc_value = []
zrange = range(-10, 10)
direction = 'superior'
search_next_disc = True
while search_next_disc:
printv('Current disc: '+str(current_disc)+' (z='+str(current_z)+'). Direction: '+direction, verbose)
try:
# get z corresponding to current disc on template
current_z_template = list_disc_z_template[current_disc]
except:
# in case reached the bottom (see issue #849)
printv('WARNING: Reached the bottom of the template. Stop searching.', verbose, 'warning')
break
# find next disc
# N.B. Do not search for C1/C2 disc (because poorly visible), use template distance instead
if not current_disc in [1]:
current_z = compute_corr_3d(src=data, target=data_template, x=xc, xshift=0, xsize=param.size_RL, y=yc, yshift=param.shift_AP, ysize=param.size_AP, z=current_z, zshift=0, zsize=param.size_IS, xtarget=xct, ytarget=yct, ztarget=current_z_template, zrange=zrange, verbose=verbose, save_suffix='_disc'+str(current_disc), gaussian_weighting=False, path_output=path_output)
# display new disc
if verbose == 2:
plt.figure(50), plt.scatter(yc+param.shift_AP_visu, current_z, c='yellow', s=50)
plt.text(yc + param.shift_AP_visu + 4, current_z, str(current_disc)+'/'+str(current_disc+1), verticalalignment='center', horizontalalignment='left', color='yellow', fontsize=15), plt.draw()
# append to main list
if direction == 'superior':
# append at the beginning
list_disc_z.insert(0, current_z)
list_disc_value.insert(0, current_disc)
elif direction == 'inferior':
# append at the end
list_disc_z.append(current_z)
list_disc_value.append(current_disc)
# adjust correcting factor based on already-identified discs
if len(list_disc_z) > 1:
# compute distance between already-identified discs
list_distance_current = (np.diff(list_disc_z) * (-1)).tolist()
# retrieve the template distance corresponding to the already-identified discs
index_disc_identified = [i for i, j in enumerate(list_disc_value_template) if j in list_disc_value[:-1]]
list_distance_template_identified = [list_distance_template[i] for i in index_disc_identified]
# divide subject and template distances for the identified discs
list_subject_to_template_distance = [float(list_distance_current[i]) / list_distance_template_identified[i] for i in range(len(list_distance_current))]
# average across identified discs to obtain an average correcting factor
correcting_factor = np.mean(list_subject_to_template_distance)
printv('.. correcting factor: '+str(correcting_factor), verbose)
else:
correcting_factor = 1
# update list_distance specific for the subject
list_distance = [int(round(list_distance_template[i] * correcting_factor)) for i in range(len(list_distance_template))]
# updated average_disc_distance (in case it is needed)
# average_disc_distance = int(round(np.mean(list_distance)))
# assign new current_z and disc value
if direction == 'superior':
try:
approx_distance_to_next_disc = list_distance[list_disc_value_template.index(current_disc-1)]
except ValueError:
printv('WARNING: Disc value not included in template. Using previously-calculated distance: '+str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z + approx_distance_to_next_disc
current_disc = current_disc - 1
elif direction == 'inferior':
try:
approx_distance_to_next_disc = list_distance[list_disc_value_template.index(current_disc)]
except:
printv('WARNING: Disc value not included in template. Using previously-calculated distance: '+str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z - approx_distance_to_next_disc
current_disc = current_disc + 1
# if current_z is larger than searching zone, switch direction (and start from initial z minus approximate distance from updated template distance)
if current_z >= nz or current_disc == 0:
printv('.. Switching to inferior direction.', verbose)
direction = 'inferior'
current_disc = init_disc[1] + 1
current_z = init_disc[0] - list_distance[list_disc_value_template.index(current_disc)]
# if current_z is lower than searching zone, stop searching
if current_z <= 0:
search_next_disc = False
# if verbose == 2:
# # close figures
# plt.figure(fig_corr), plt.close()
# plt.figure(fig_pattern), plt.close()
# if upper disc is not 1, add disc above top disc based on mean_distance_adjusted
upper_disc = min(list_disc_value)
# if not upper_disc == 1:
printv('Adding top disc based on adjusted template distance: #'+str(upper_disc-1), verbose)
approx_distance_to_next_disc = list_distance[list_disc_value_template.index(upper_disc-1)]
next_z = max(list_disc_z) + approx_distance_to_next_disc
printv('.. approximate distance: '+str(approx_distance_to_next_disc), verbose)
# make sure next disc does not go beyond FOV in superior direction
if next_z > nz:
list_disc_z.insert(0, nz)
else:
list_disc_z.insert(0, next_z)
# assign disc value
list_disc_value.insert(0, upper_disc-1)
# Label segmentation
label_segmentation(fname_seg, list_disc_z, list_disc_value, verbose=verbose)
# save figure
if verbose == 2:
plt.figure(50), plt.savefig(path_output + 'fig_anat_straight_with_labels.png')
if nt > 1:
sct.printv('ERROR: your input image needs to be 3D in order to be segmented.', 1, 'error')
path_fname, file_fname, ext_fname = sct.extract_fname(input_filename)
# if centerline or mask is asked using viewer
if use_viewer:
# make sure image is in SAL orientation, as it is the orientation used by PropSeg
image_input_orientation = orientation(image_input, get=True, verbose=False)
reoriented_image_filename = 'tmp.' + sct.add_suffix(file_fname + ext_fname, "_SAL")
path_tmp_viewer = sct.tmp_create(verbose=verbose)
sct.run('sct_image -i ' + input_filename + ' -o ' + path_tmp_viewer + reoriented_image_filename + ' -setorient SAL -v 0', verbose=False)
from sct_viewer import ClickViewer
image_input_reoriented = Image(path_tmp_viewer + reoriented_image_filename)
viewer = ClickViewer(image_input_reoriented)
viewer.help_url = 'https://sourceforge.net/p/spinalcordtoolbox/wiki/correction_PropSeg/attachment/propseg_viewer.png'
if use_viewer == "mask":
viewer.number_of_slices = 3
viewer.gap_inter_slice = int(10 / pz)
if viewer.gap_inter_slice == 0:
viewer.gap_inter_slice = 1
viewer.calculate_list_slices()
#else:
# viewer.gap_inter_slice = 3
# start the viewer that ask the user to enter a few points along the spinal cord
mask_points = viewer.start()
if mask_points:
# create the mask containing either the three-points or centerline mask for initialization
mask_filename = sct.add_suffix(reoriented_image_filename, "_mask_viewer")