def roi_extract(self, inputs, outputs):
'''
'''
# check if we have all required input data
# we need at least:
# - outputs['fibers_mapped'] == Track file in T1 space with mapped scalars
if not os.path.exists(outputs['fibers_final']):
c.error(Colors.RED + 'Could not find ' + Colors.YELLOW +
outputs['fibers_final'] + Colors.RED +
' but we really need it to start with stage 6!!' +
Colors._CLEAR)
sys.exit(2)
s = io.loadTrk(outputs['fibers_final'])
tracks = s[0]
header = s[1]
scalarNames = header['scalar_name'].tolist()
labels = {}
# check if the segmentation is mapped
try:
seg_index = scalarNames.index('segmentation')
except:
c.error(Colors.RED + 'Could not find ' + Colors.YELLOW +
'segmentation' + Colors.RED +
' as a mapped scalar but we really need it!')
sys.exit(2)
# create the roi subfolder
if not os.path.exists(outputs['roi']):
os.mkdir(outputs['roi'])
# parse the color table
lut = fyborg.colortable.freesurfer.split('\n')
colors = {}
for color in lut:
if not color or color[0] == '#':
continue
splitted_line = color.split(' ')
splitted_line = filter(None, splitted_line)
colors[splitted_line[0]] = splitted_line[1]
# loop through tracks
for i, t in enumerate(tracks):
tCoordinates = t[0]
tScalars = t[1]
# grab the scalars for each point
for scalar in tScalars:
# but only the label value
label_value = str(int(scalar[seg_index]))
if not label_value in labels:
labels[label_value] = []
if not i in labels[label_value]:
# store the unique fiber id for this label
labels[label_value].append(i)
# now loop through all detected labels
for l in labels:
new_tracks = []
for t_id in labels[l]:
# grab the fiber + scalars
current_fiber = tracks[t_id]
new_tracks.append(current_fiber)
# now store the trk file
trk_outputfile = l + '_' + colors[l] + '.trk'
nii_outputfile = l + '_' + colors[l] + '.nii.gz'
c.info(Colors.YELLOW + ' Creating fiber ROI ' + Colors.PURPLE +
trk_outputfile + Colors.YELLOW + '!' + Colors._CLEAR)
io.saveTrk(os.path.join(outputs['roi'], trk_outputfile),
new_tracks, header, None, True)
# also create a roi label volume for this label value
c.info(Colors.YELLOW + ' Creating NII ROI ' + Colors.PURPLE +
nii_outputfile + Colors.YELLOW + '!' + Colors._CLEAR)
cmd = 'ss;'
cmd += 'chb-fsstable;'
cmd += 'mri_binarize --i ' + outputs[
'segmentation'] + ' --o ' + os.path.join(
outputs['roi'],
nii_outputfile) + ' --match ' + l + ' --binval ' + l + ';'
self.__logger.debug(cmd)
sp = subprocess.Popen(["/bin/bash", "-i", "-c", cmd],
stdout=sys.stdout)
sp.communicate()
def roi_extract( self, inputs, outputs ):
'''
'''
# check if we have all required input data
# we need at least:
# - outputs['fibers_mapped'] == Track file in T1 space with mapped scalars
if not os.path.exists( outputs['fibers_final'] ):
c.error( Colors.RED + 'Could not find ' + Colors.YELLOW + outputs['fibers_final'] + Colors.RED + ' but we really need it to start with stage 6!!' + Colors._CLEAR )
sys.exit( 2 )
s = io.loadTrk( outputs['fibers_final'] )
tracks = s[0]
header = s[1]
scalarNames = header['scalar_name'].tolist()
labels = {}
# check if the segmentation is mapped
try:
seg_index = scalarNames.index( 'segmentation' )
except:
c.error( Colors.RED + 'Could not find ' + Colors.YELLOW + 'segmentation' + Colors.RED + ' as a mapped scalar but we really need it!' )
sys.exit( 2 )
# create the roi subfolder
if not os.path.exists( outputs['roi'] ):
os.mkdir( outputs['roi'] )
# parse the color table
lut = fyborg.colortable.freesurfer.split( '\n' )
colors = {}
for color in lut:
if not color or color[0] == '#':
continue
splitted_line = color.split( ' ' )
splitted_line = filter( None, splitted_line )
colors[splitted_line[0]] = splitted_line[1]
# loop through tracks
for i, t in enumerate( tracks ):
tCoordinates = t[0]
tScalars = t[1]
# grab the scalars for each point
for scalar in tScalars:
# but only the label value
label_value = str( int( scalar[seg_index] ) )
if not label_value in labels:
labels[label_value] = []
if not i in labels[label_value]:
# store the unique fiber id for this label
labels[label_value].append( i )
# now loop through all detected labels
for l in labels:
new_tracks = []
for t_id in labels[l]:
# grab the fiber + scalars
current_fiber = tracks[t_id]
new_tracks.append( current_fiber )
# now store the trk file
trk_outputfile = l + '_' + colors[l] + '.trk'
nii_outputfile = l + '_' + colors[l] + '.nii'
c.info( Colors.YELLOW + ' Creating fiber ROI ' + Colors.PURPLE + trk_outputfile + Colors.YELLOW + '!' + Colors._CLEAR )
io.saveTrk( os.path.join( outputs['roi'], trk_outputfile ), new_tracks, header, None, True )
# also create a roi label volume for this label value
c.info( Colors.YELLOW + ' Creating NII ROI ' + Colors.PURPLE + nii_outputfile + Colors.YELLOW + '!' + Colors._CLEAR )
cmd = 'ss;'
cmd += 'chb-fsstable;'
cmd += 'mri_binarize --i ' + outputs['segmentation'] + ' --o ' + os.path.join( outputs['roi'], nii_outputfile ) + ' --match ' + l + ' --binval ' + l + ';'
self.__logger.debug( cmd )
sp = subprocess.Popen( ["/bin/bash", "-i", "-c", cmd], stdout=sys.stdout )
sp.communicate()
def connectivity(self, inputs, outputs, cortex_only):
'''
Generate connectivity matrices using mapped values.
'''
# check if we have all required input data
# we need at least:
# - outputs['fibers_mapped'] == Track file in T1 space with mapped scalars
if not os.path.exists(outputs['fibers_final']):
c.error(Colors.RED + 'Could not find ' + Colors.YELLOW +
outputs['fibers_final'] + Colors.RED +
' but we really need it to start with stage 5!!' +
Colors._CLEAR)
sys.exit(2)
s = io.loadTrk(outputs['fibers_final'])
tracks = s[0]
header = s[1]
scalarNames = header['scalar_name'].tolist()
matrix = {}
indices = {}
# check if the segmentation is mapped
try:
indices['segmentation'] = scalarNames.index('segmentation')
except:
c.error(Colors.RED + 'Could not find ' + Colors.YELLOW +
'segmentation' + Colors.RED +
' as a mapped scalar but we really need it!')
sys.exit(2)
if cortex_only:
labels = [
2012, 2019, 2032, 2014, 2020, 2018, 2027, 2028, 2003, 2024,
2017, 2026, 2002, 2023, 2010, 2022, 2031, 2029, 2008, 2025,
2005, 2021, 2011, 2013, 2007, 2016, 2006, 2033, 2009, 2015,
2001, 2030, 2034, 2035, 1012, 1019, 1032, 1014, 1020, 1018,
1027, 1028, 1003, 1024, 1017, 1026, 1002, 1023, 1010, 1022,
1031, 1029, 1008, 1025, 1005, 1021, 1011, 1013, 1007, 1016,
1006, 1033, 1009, 1015, 1001, 1030, 1034, 1035
]
else:
labels = [
2012, 2019, 2032, 2014, 2020, 2018, 2027, 2028, 2003, 2024,
2017, 2026, 2002, 2023, 2010, 2022, 2031, 2029, 2008, 2025,
2005, 2021, 2011, 2013, 2007, 2016, 2006, 2033, 2009, 2015,
2001, 2030, 2034, 2035, 49, 50, 51, 52, 58, 53, 54, 1012, 1019,
1032, 1014, 1020, 1018, 1027, 1028, 1003, 1024, 1017, 1026,
1002, 1023, 1010, 1022, 1031, 1029, 1008, 1025, 1005, 1021,
1011, 1013, 1007, 1016, 1006, 1033, 1009, 1015, 1001, 1030,
1034, 1035, 10, 11, 12, 13, 26, 17, 18, 16
]
c.info(
Colors.YELLOW +
' Getting ready to create connectivity matrices for the following labels: '
+ Colors.PURPLE + str(labels) + Colors._CLEAR)
c.info(
Colors.YELLOW +
' Note: Mapped scalar values along the points will be averaged for each fiber track.'
+ Colors._CLEAR)
# create matrices for the attached scalars
for i, s in enumerate(scalarNames):
if i >= header['n_scalars']:
break
if not s or s == 'segmentation':
continue
# this is a scalar value for which a matrix will be created
matrix[s] = np.zeros([len(labels), len(labels)])
indices[s] = scalarNames.index(s)
c.info(Colors.YELLOW + ' Preparing matrix (' + Colors.PURPLE +
'[' + str(len(labels)) + 'x' + str(len(labels)) + ']' +
Colors.YELLOW + ') for ' + Colors.PURPLE + s +
Colors.YELLOW + ' values!' + Colors._CLEAR)
if s == 'adc':
s = 'inv_adc'
matrix[s] = np.zeros([len(labels), len(labels)])
indices[s] = scalarNames.index('adc')
c.info(Colors.YELLOW + ' Preparing matrix (' + Colors.PURPLE +
'[' + str(len(labels)) + 'x' + str(len(labels)) + ']' +
Colors.YELLOW + ') for ' + Colors.PURPLE + s +
Colors.YELLOW + ' values!' + Colors._CLEAR)
# always create one for the fiber counts
matrix['fibercount'] = np.zeros([len(labels), len(labels)])
indices['fibercount'] = 0
c.info(Colors.YELLOW + ' Preparing matrix (' + Colors.PURPLE + '[' +
str(len(labels)) + 'x' + str(len(labels)) + ']' +
Colors.YELLOW + ') for ' + Colors.PURPLE + 'fibercount' +
Colors.YELLOW + ' values!' + Colors._CLEAR)
c.info(Colors.YELLOW + ' Analyzing fibers of ' + Colors.PURPLE +
os.path.split(outputs['fibers_final'])[1] + Colors.YELLOW +
'..' + Colors._CLEAR)
for tCounter, t in enumerate(tracks):
tCoordinates = t[0]
tScalars = t[1]
# find the segmentation labels for the start and end points
start_label = tScalars[0, indices['segmentation']]
end_label = tScalars[-1, indices['segmentation']]
try:
# now grab the index of the labels in our label list
start_index = labels.index(start_label)
end_index = labels.index(end_label)
except:
# this label is not monitored, so ignore this track
continue
# loop through all different scalars
for m in matrix:
# calculate the mean for each track
value = np.mean(tScalars[:, indices[m]])
if m == 'inv_adc':
# invert the value since it is 1-ADC
value = 1 / value
elif m == 'fibercount':
# in the case of fibercount, add 1
value = 1
# store value in the matrix
matrix[m][start_index, end_index] += value
if not start_index == end_index:
matrix[m][end_index, start_index] += value
# fiber loop is done, all values are stored
# now normalize the matrices
np.seterr(all='ignore') # avoid div by 0 warnings
cbar = None
for m in matrix:
if not m == 'fibercount':
# normalize it
matrix[m][:] /= matrix['fibercount']
matrix[m] = np.nan_to_num(matrix[m])
# store the matrix
c.info(Colors.YELLOW + ' Storing ' + Colors.PURPLE + m +
Colors.YELLOW + ' connectivity matrix as ' + Colors.PURPLE +
os.path.split(outputs['matrix_' + m])[1] + Colors.YELLOW +
'!' + Colors._CLEAR)
np.savetxt(outputs['matrix_' + m], matrix[m], delimiter='\t')
# store a picture
picture_path = os.path.splitext(
os.path.split(outputs['matrix_' + m])[1])[0] + '.png'
c.info(Colors.YELLOW + ' Generating ' + Colors.PURPLE + m +
' image' + Colors.YELLOW + ' as ' + Colors.PURPLE +
picture_path + Colors.YELLOW + '!' + Colors._CLEAR)
img = plot.imshow(matrix[m], interpolation='nearest')
img.set_cmap('jet')
img.set_norm(LogNorm())
img.axes.get_xaxis().set_visible(False)
img.axes.get_yaxis().set_visible(False)
if not cbar:
cbar = plot.colorbar()
cbar.set_label(m)
cbar.set_ticks([])
plot.savefig(
os.path.join(
os.path.split(outputs['matrix_' + m])[0], picture_path))
np.seterr(all='warn') # reactivate div by 0 warnings
# now store the matlab version as well
c.info(Colors.YELLOW + ' Storing ' + Colors.PURPLE +
'matlab data bundle' + Colors.YELLOW + ' containing ' +
Colors.PURPLE + 'all matrices' + Colors.YELLOW + ' as ' +
Colors.PURPLE + os.path.split(outputs['matrix_all'])[1] +
Colors.YELLOW + '!' + Colors._CLEAR)
scipy.io.savemat(outputs['matrix_all'], matrix, oned_as='row')
def connectivity( self, inputs, outputs, cortex_only ):
'''
Generate connectivity matrices using mapped values.
'''
# check if we have all required input data
# we need at least:
# - outputs['fibers_mapped'] == Track file in T1 space with mapped scalars
if not os.path.exists( outputs['fibers_final'] ):
c.error( Colors.RED + 'Could not find ' + Colors.YELLOW + outputs['fibers_final'] + Colors.RED + ' but we really need it to start with stage 5!!' + Colors._CLEAR )
sys.exit( 2 )
s = io.loadTrk( outputs['fibers_final'] )
tracks = s[0]
header = s[1]
scalarNames = header['scalar_name'].tolist()
matrix = {}
indices = {}
# check if the segmentation is mapped
try:
indices['segmentation'] = scalarNames.index( 'segmentation' )
except:
c.error( Colors.RED + 'Could not find ' + Colors.YELLOW + 'segmentation' + Colors.RED + ' as a mapped scalar but we really need it!' )
sys.exit( 2 )
if cortex_only:
labels = [2012, 2019, 2032, 2014, 2020, 2018, 2027, 2028, 2003, 2024, 2017, 2026, 2002, 2023, 2010, 2022, 2031, 2029, 2008, 2025, 2005, 2021, 2011, 2013, 2007, 2016, 2006, 2033, 2009, 2015, 2001, 2030, 2034, 2035, 1012, 1019, 1032, 1014, 1020, 1018, 1027, 1028, 1003, 1024, 1017, 1026, 1002, 1023, 1010, 1022, 1031, 1029, 1008, 1025, 1005, 1021, 1011, 1013, 1007, 1016, 1006, 1033, 1009, 1015, 1001, 1030, 1034, 1035]
else:
labels = [2012, 2019, 2032, 2014, 2020, 2018, 2027, 2028, 2003, 2024, 2017, 2026, 2002, 2023, 2010, 2022, 2031, 2029, 2008, 2025, 2005, 2021, 2011, 2013, 2007, 2016, 2006, 2033, 2009, 2015, 2001, 2030, 2034, 2035, 49, 50, 51, 52, 58, 53, 54, 1012, 1019, 1032, 1014, 1020, 1018, 1027, 1028, 1003, 1024, 1017, 1026, 1002, 1023, 1010, 1022, 1031, 1029, 1008, 1025, 1005, 1021, 1011, 1013, 1007, 1016, 1006, 1033, 1009, 1015, 1001, 1030, 1034, 1035, 10, 11, 12, 13, 26, 17, 18, 16]
c.info( Colors.YELLOW + ' Getting ready to create connectivity matrices for the following labels: ' + Colors.PURPLE + str( labels ) + Colors._CLEAR )
c.info( Colors.YELLOW + ' Note: Mapped scalar values along the points will be averaged for each fiber track.' + Colors._CLEAR )
# create matrices for the attached scalars
for i, s in enumerate( scalarNames ):
if i >= header['n_scalars']:
break
if not s or s == 'segmentation':
continue
# this is a scalar value for which a matrix will be created
matrix[s] = np.zeros( [len( labels ), len( labels )] )
indices[s] = scalarNames.index( s )
c.info( Colors.YELLOW + ' Preparing matrix (' + Colors.PURPLE + '[' + str( len( labels ) ) + 'x' + str( len( labels ) ) + ']' + Colors.YELLOW + ') for ' + Colors.PURPLE + s + Colors.YELLOW + ' values!' + Colors._CLEAR )
if s == 'adc':
s = 'inv_adc'
matrix[s] = np.zeros( [len( labels ), len( labels )] )
indices[s] = scalarNames.index( 'adc' )
c.info( Colors.YELLOW + ' Preparing matrix (' + Colors.PURPLE + '[' + str( len( labels ) ) + 'x' + str( len( labels ) ) + ']' + Colors.YELLOW + ') for ' + Colors.PURPLE + s + Colors.YELLOW + ' values!' + Colors._CLEAR )
# always create one for the fiber counts
matrix['fibercount'] = np.zeros( [len( labels ), len( labels )] )
indices['fibercount'] = 0
c.info( Colors.YELLOW + ' Preparing matrix (' + Colors.PURPLE + '[' + str( len( labels ) ) + 'x' + str( len( labels ) ) + ']' + Colors.YELLOW + ') for ' + Colors.PURPLE + 'fibercount' + Colors.YELLOW + ' values!' + Colors._CLEAR )
c.info( Colors.YELLOW + ' Analyzing fibers of ' + Colors.PURPLE + os.path.split( outputs['fibers_final'] )[1] + Colors.YELLOW + '..' + Colors._CLEAR )
for tCounter, t in enumerate( tracks ):
tCoordinates = t[0]
tScalars = t[1]
# find the segmentation labels for the start and end points
start_label = tScalars[0, indices['segmentation']]
end_label = tScalars[-1, indices['segmentation']]
try:
# now grab the index of the labels in our label list
start_index = labels.index( start_label )
end_index = labels.index( end_label )
except:
# this label is not monitored, so ignore this track
continue
# loop through all different scalars
for m in matrix:
# calculate the mean for each track
value = np.mean( tScalars[:, indices[m]] )
if m == 'inv_adc':
# invert the value since it is 1-ADC
value = 1 / value
elif m == 'fibercount':
# in the case of fibercount, add 1
value = 1
# store value in the matrix
matrix[m][start_index, end_index] += value
if not start_index == end_index:
matrix[m][end_index, start_index] += value
# fiber loop is done, all values are stored
# now normalize the matrices
np.seterr( all='ignore' ) # avoid div by 0 warnings
cbar = None
for m in matrix:
if not m == 'fibercount':
# normalize it
matrix[m][:] /= matrix['fibercount']
matrix[m] = np.nan_to_num( matrix[m] )
# store the matrix
c.info( Colors.YELLOW + ' Storing ' + Colors.PURPLE + m + Colors.YELLOW + ' connectivity matrix as ' + Colors.PURPLE + os.path.split( outputs['matrix_' + m] )[1] + Colors.YELLOW + '!' + Colors._CLEAR )
np.savetxt( outputs['matrix_' + m], matrix[m], delimiter='\t' )
# store a picture
picture_path = os.path.splitext( os.path.split( outputs['matrix_' + m] )[1] )[0] + '.png'
c.info( Colors.YELLOW + ' Generating ' + Colors.PURPLE + m + ' image' + Colors.YELLOW + ' as ' + Colors.PURPLE + picture_path + Colors.YELLOW + '!' + Colors._CLEAR )
img = plot.imshow( matrix[m], interpolation='nearest' )
img.set_cmap( 'jet' )
img.set_norm( LogNorm() )
img.axes.get_xaxis().set_visible( False )
img.axes.get_yaxis().set_visible( False )
if not cbar:
cbar = plot.colorbar()
cbar.set_label( m )
cbar.set_ticks( [] )
plot.savefig( os.path.join( os.path.split( outputs['matrix_' + m] )[0], picture_path ) )
np.seterr( all='warn' ) # reactivate div by 0 warnings
# now store the matlab version as well
c.info( Colors.YELLOW + ' Storing ' + Colors.PURPLE + 'matlab data bundle' + Colors.YELLOW + ' containing ' + Colors.PURPLE + 'all matrices' + Colors.YELLOW + ' as ' + Colors.PURPLE + os.path.split( outputs['matrix_all'] )[1] + Colors.YELLOW + '!' + Colors._CLEAR )
scipy.io.savemat( outputs['matrix_all'], matrix, oned_as='row' )
