def methods_write_json_redcap_ants_ct_instrument(ants_ct_info, verbose):
""" Write ANTs CT Instrument to JSON output file"""
df_stats = labels.properties(ants_ct_info['segment']['segment'])
dict_redcap = OrderedDict(
(('subject_id',
ants_ct_info['base']['subject_id']), ('ants_ct_analyst',
getpass.getuser()),
('ants_ct_datetime',
'{:%Y-%b-%d %H:%M:%S}'.format(datetime.datetime.now())),
('ants_ct_csf_volume_mm3',
'{0:4.3f}'.format(df_stats.iloc[0]['volume_mm3'])),
('ants_ct_gm_cortical_volume_mm3',
'{0:4.3f}'.format(df_stats.iloc[1]['volume_mm3'])),
('ants_ct_wm_volume_mm3',
'{0:4.3f}'.format(df_stats.iloc[2]['volume_mm3'])),
('ants_ct_gm_subcortical_volume_mm3',
'{0:4.3f}'.format(df_stats.iloc[3]['volume_mm3'])),
('ants_ct_brainstem_volume_mm3',
'{0:4.3f}'.format(df_stats.iloc[4]['volume_mm3'])),
('ants_ct_cerebellum_volume_mm3',
'{0:4.3f}'.format(df_stats.iloc[5]['volume_mm3']))))
with open(ants_ct_info['results']['json'], 'w') as outfile:
json.dump(dict_redcap,
outfile,
indent=4,
ensure_ascii=True,
sort_keys=False)
if verbose:
print(
json.dumps(dict_redcap,
indent=4,
ensure_ascii=True,
sort_keys=False))
return
def methods_03_stats(input_dir, lower_threshold=0.5, upper_threshold=numpy.inf, min_lesion_volume_voxels=10, verbose=False):
cenc_dirs = cenc.directories(input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
util.mkcd_dir([cenc_dirs['wmlesions']['dirs']['stats']], True)
wm_lesions_stats_filename = os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.csv')
wm_lesions_pmap_stats_filename = os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.csv')
# Mask LPA Probability Mask and remove NaN
command = [['fslmaths', os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'], 'ples_lpa_mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas', os.path.join(cenc_dirs['wmlesions']['dirs']['input'], 'mask.nii.gz'), '-nan',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz')
]]
for ii in command:
util.iw_subprocess(ii, verbose, verbose, False)
# Create labels file from LPA probability map
labels.label_connected_components( os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
lower_threshold, upper_threshold )
# Measure properties of labels
labels.properties(os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
None, False, ['volume_mm3'],
wm_lesions_stats_filename,
limits_volume_voxels=[min_lesion_volume_voxels, numpy.inf],
limits_bb_volume_voxels=[0, numpy.inf], limits_fill_factor=[0, 1], sort='volume_mm3',
verbose=verbose, verbose_nlines=20)
# Keep labels greater than 10 mm^3. Limit is set above
labels.keep(os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
[],
wm_lesions_stats_filename,
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz')
)
# 1) Create a WM Lesions mask
# 2) Mask LPA Registered T2 FLAIR
command = [ ['fslmaths', os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
'-bin', os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_mask.nii.gz')
],
['fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'], 'mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas', os.path.join(cenc_dirs['wmlesions']['dirs']['input'], 'mask.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_t2flair.nii.gz')
]
]
for ii in command:
util.iw_subprocess(ii, verbose, verbose, False)
# Measure Pmaps stats labels
df_pmap_measure = labels.measure( os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz'))
df_pmap_measure.to_csv(wm_lesions_pmap_stats_filename, index=False)
# Write out JSON file with RedCap Instrument
methods_write_json_redcap_instrument(input_dir, wm_lesions_stats_filename, wm_lesions_pmap_stats_filename, verbose)