def run(self, args):
mask = mdt.load_brain_mask(os.path.realpath(args.mask))
file_names = []
for file in args.input_files:
file_names.extend(glob.glob(file))
for file in file_names:
mdt.apply_mask_to_file(file, mask)
def run(self, args, extra_args):
mask = mdt.load_brain_mask(
nifti_filepath_resolution(os.path.realpath(args.mask)))
file_names = []
for file in args.input_files:
file_names.extend(glob.glob(file))
for file in file_names:
if args.overwrite:
mdt.apply_mask_to_file(file, mask)
else:
folder, basename, ext = split_image_path(
nifti_filepath_resolution(os.path.realpath(file)))
mdt.apply_mask_to_file(file,
mask,
output_fname=os.path.join(
folder, basename + '_masked' + ext))
def view_mask(self):
mask = np.expand_dims(load_brain_mask(self.selectedOutputText.text()),
axis=3)
image_data = load_nifti(self.selectedImageText.text()).get_data()
masked_image = image_data * mask
data = SimpleDataInfo({
'Masked': masked_image,
'DWI': image_data,
'Mask': mask
})
config = MapPlotConfig()
config.dimension = 2
config.slice_index = image_data.shape[2] // 2
config.maps_to_show = ['DWI', 'Masked', 'Mask']
start_gui(data=data, config=config, app_exec=False)
def get_mask(dataset_name):
if dataset_name == 'mgh':
mask_name = 'mgh_1003_slice_44_mask'
else:
mask_name = 'rheinland_v3a_1_ms20_slice_36_mask'
return mdt.load_brain_mask(input_pjoin(dataset_name, mask_name))
from scipy.ndimage import binary_erosion
import mdt
__author__ = 'Robbert Harms'
__date__ = '2018-12-20'
__maintainer__ = 'Robbert Harms'
__email__ = '*****@*****.**'
__licence__ = 'LGPL v3'
output_pjoin = mdt.make_path_joiner(
'/home/robbert/phd-data/papers/uncertainty_paper/registration/')
mask = mdt.load_brain_mask(
'/usr/share/data/fsl-mni152-templates/FMRIB58_FA_1mm.nii.gz')
mask = binary_erosion(mask, iterations=1)
maps = {}
subjects_to_load = ['mgh_1005', 'mgh_1016', 'mgh_1017']
for subject in subjects_to_load:
point_map = mdt.load_nifti(
output_pjoin(subject, 'warped_BinghamNODDI_r1_w_in0.w')).get_data()
std_map = mdt.load_nifti(
output_pjoin(subject,
'warped_BinghamNODDI_r1_w_in0.w.std')).get_data()
maps[subject + '.std'] = std_map
maps[subject] = point_map
mdt.apply_mask(maps, mask)
def pearson_correlation_coefficient(x, y, dataset, model_name, data_type):
coef_mat = np.corrcoef(np.squeeze(x), np.squeeze(y))
print(dataset, model_name, data_type, coef_mat[0, 1])
percentages = []
for dataset in datasets:
for model_name in model_names:
print(dataset, model_name)
mle, mle_std = get_mle_results(dataset, model_name)
mcmc, mcmc_std = get_mcmc_results(dataset, model_name)
wm_mask = mdt.load_brain_mask(output_base_pjoin(dataset, 'wm_mask'))
# wm_mask = mdt.load_brain_mask(masks[dataset])
# wm_mask *= (np.abs(mle - mcmc) < 0.01)[..., 0]
# wm_mask *= (mle >= 0.1)[..., 0]
# wm_mask *= (mle <= 0.9)[..., 0]
# wm_mask *= (mcmc >= 0.1)[..., 0]
# wm_mask *= (mcmc <= 0.9)[..., 0]
# wm_mask = mle >= 0.1
# wm_mask *= (np.abs(mle_std - mcmc_std) < 0.01)[..., 0]
# items = {'mle': mle, 'mle_std': mle_std, 'mcmc': mcmc, 'mcmc_std': mcmc_std,
# 'p_diff': np.abs(mle - mcmc), 'std_diff': np.abs(mle_std - mcmc_std),
# 'std_threshold_map': np.abs(mle_std - mcmc_std) > 0.01
