def vanderbilt_processing(data_path):
"""
get a segmentation image of the spinal cord an of the graymatter from a three level mask
:param data_path: path to the data
:return:
"""
im_ext = '.nii.gz'
if data_path[-1] == '/':
data_path = data_path[:-1]
original_path = os.path.abspath('.')
os.chdir(data_path)
for subject_dir in os.listdir('.'):
if os.path.isdir(subject_dir):
os.chdir(subject_dir)
sc_seg_list = []
gm_seg_list = []
im_list = []
for file_name in os.listdir('.'):
if 'seg' in file_name:
mask_im = Image(file_name)
sc_seg_im = mask_im.copy()
sc_seg_im.file_name = sct.extract_fname(file_name)[1][:-4] + '_manual_sc_seg'
sc_seg_im.ext = '.nii.gz'
sc_seg_im.data = (sc_seg_im.data > 0).astype(int)
sc_seg_im.save()
sc_seg_list.append(sc_seg_im.file_name + sc_seg_im.ext)
gm_seg_im = mask_im.copy()
gm_seg_im.file_name = sct.extract_fname(file_name)[1][:-4] + '_manual_gm_seg'
gm_seg_im.ext = '.nii.gz'
gm_seg_im.data = (gm_seg_im.data > 1).astype(int)
gm_seg_im.save()
gm_seg_list.append(gm_seg_im.file_name + gm_seg_im.ext)
im_list.append(file_name[:17] + im_ext)
# merging the slice images into a 3D image
cmd_merge = 'fslmerge -z '
im_name = subject_dir + '_im.nii.gz'
cmd_merge_im = cmd_merge + im_name
gmseg_name = subject_dir + '_manual_gmseg.nii.gz'
cmd_merge_gm_seg = cmd_merge + gmseg_name
scseg_name = subject_dir + '_manual_scseg.nii.gz'
cmd_merge_sc_seg = cmd_merge + scseg_name
for im_i, gm_i, sc_i in zip(im_list, gm_seg_list, sc_seg_list):
cmd_merge_im += ' ' + im_i
cmd_merge_gm_seg += ' ' + gm_i
cmd_merge_sc_seg += ' ' + sc_i
sct.run(cmd_merge_im)
sct.run(cmd_merge_gm_seg)
sct.run(cmd_merge_sc_seg)
label_slices = [im_slice.split('_')[-1][2:4] for im_slice in im_list]
i_slice_to_level = {0: 6, 1: 6, 2: 6, 3: 6, 4: 6, 5: 5, 6: 5, 7: 5, 8: 5, 9: 5, 10: 4, 11: 4, 12: 4, 13: 4, 14: 4, 15: 3, 16: 3, 17: 3, 18: 3, 19: 3, 20: 3, 21: 2, 22: 2, 23: 2, 24: 2, 25: 1, 26: 1, 27: 1, 28: 1, 29: 1}
level_dat = np.zeros((mask_im.data.shape[0], mask_im.data.shape[1], len(im_list)))
for i, l_slice in enumerate(label_slices):
i_slice = int(l_slice) - 1
level_dat.T[:][:][i] = i_slice_to_level[i_slice]
Image(param=level_dat, absolutepath=subject_dir + '_levels.nii.gz').save()
# resampling
resample_image(im_name)
resample_image(gmseg_name, binary=True)
resample_image(scseg_name, binary=True)
# organizing data
sct.run('mkdir original_data/')
sct.run('mkdir extracted_data/')
sct.run('mkdir 3d_data/')
sct.run('mkdir 3d_resampled_data/')
sct.run('mkdir dic_data/')
for file_name in os.listdir('.'):
if '_manual_gm_seg' in file_name and 'sl' in file_name:
sct.run('cp ' + file_name + ' dic_data/')
sct.run('cp ' + file_name[:-21] + '.nii.gz dic_data/')
for file_name in os.listdir('.'):
if 'sl' in file_name and 'manual' not in file_name:
sct.run('mv ' + file_name + ' original_data/')
elif 'manual' in file_name and 'sl' in file_name:
sct.run('mv ' + file_name + ' extracted_data/')
elif 'resampled.nii' in file_name:
sct.run('mv ' + file_name + ' 3d_resampled_data/')
elif '_sl' not in file_name and not os.path.isdir(os.path.abspath('.') + '/' + file_name) or 'level' in file_name:
sct.run('mv ' + file_name + ' 3d_data/')
elif not os.path.isdir(os.path.abspath('.') + '/' + file_name):
sct.run('mv ' + file_name + ' original_data/')
os.chdir('..')
os.chdir(original_path)
def amu_processing(data_path):
"""
get a segmentation image of the spinal cord an of the graymatter from a three level mask
:param data_path: path to the data
:return:
"""
im_ext = '.nii'
if data_path[-1] == '/':
data_path = data_path[:-1]
original_path = os.path.abspath('.')
os.chdir(data_path)
for subject_dir in os.listdir('.'):
subject_path = data_path + '/' + subject_dir
if os.path.isdir(subject_dir):
os.chdir(subject_dir)
sc_seg_list = []
gm_seg_list = []
im_list = []
for file_name in os.listdir('.'):
ext = sct.extract_fname(file_name)[2]
if 'mask' in file_name and ext != '.hdr':
mask_im = Image(file_name)
sc_seg_im = mask_im.copy()
sc_seg_im.file_name = sct.extract_fname(file_name)[1][:-5] + '_manual_sc_seg'
sc_seg_im.ext = '.nii.gz'
sc_seg_im.data = (sc_seg_im.data > 1).astype(int)
# sc_seg_im = Image(param=sc_seg, absolutepath=subject_path + '/' + sct.extract_fname(file_name)[1][:-5] + '_manual_sc_seg.nii.gz')
# sc_seg_im.orientation = 'RPI'
sc_seg_im.save()
sc_seg_list.append(sc_seg_im.file_name + sc_seg_im.ext)
gm_seg_im = mask_im.copy()
gm_seg_im.file_name = sct.extract_fname(file_name)[1][:-5] + '_manual_gm_seg'
gm_seg_im.ext = '.nii.gz'
gm_seg_im.data = (gm_seg_im.data > 2).astype(int)
# gm_seg_im = Image(param=gm_seg, absolutepath=subject_path + '/' + sct.extract_fname(file_name)[1][:-5] + '_manual_gm_seg.nii.gz')
# gm_seg_im.orientation = 'RPI'
gm_seg_im.save()
gm_seg_list.append(gm_seg_im.file_name + gm_seg_im.ext)
im_list.append(file_name[:2] + im_ext)
# merging the slice images into a 3D image
im_list.reverse()
gm_seg_list.reverse()
sc_seg_list.reverse()
cmd_merge = 'fslmerge -z '
im_name = subject_dir + '_im.nii.gz '
cmd_merge_im = cmd_merge + im_name
gmseg_name = subject_dir + '_manual_gmseg.nii.gz '
cmd_merge_gm_seg = cmd_merge + gmseg_name
scseg_name = subject_dir + '_manual_scseg.nii.gz '
cmd_merge_sc_seg = cmd_merge + scseg_name
for im_i, gm_i, sc_i in zip(im_list, gm_seg_list, sc_seg_list):
cmd_merge_im += im_i + ' '
cmd_merge_gm_seg += gm_i + ' '
cmd_merge_sc_seg += sc_i + ' '
sct.run(cmd_merge_im)
sct.run(cmd_merge_gm_seg)
sct.run(cmd_merge_sc_seg)
# creating a level image
level_label = {0: '', 1: 'C1', 2: 'C2', 3: 'C3', 4: 'C4', 5: 'C5', 6: 'C6', 7: 'C7', 8: 'T1', 9: 'T2', 10: 'T3', 11: 'T4', 12: 'T5', 13: 'T6'}
level_dat = np.zeros((mask_im.data.shape[0], mask_im.data.shape[1], len(im_list)))
for i, im_i_name in enumerate(im_list):
level_dat.T[:][:][i] = get_key_from_val(level_label, im_i_name[:2].upper())
Image(param=level_dat, absolutepath=subject_dir + '_levels.nii.gz').save()
# resampling
resample_image(im_name)
resample_image(gmseg_name, binary=True, thr=0.45)
resample_image(scseg_name, binary=True, thr=0.55)
# organizing data
sct.run('mkdir original_data/')
sct.run('mkdir extracted_data/')
sct.run('mkdir 3d_data/')
sct.run('mkdir 3d_resampled_data/')
for file_name in os.listdir('.'):
if 'mask' in file_name:
sct.run('mv ' + file_name + ' original_data/')
elif 'manual' in file_name and 'G1' not in file_name:
sct.run('mv ' + file_name + ' extracted_data/')
elif 'resampled.nii' in file_name:
sct.run('mv ' + file_name + ' 3d_resampled_data/')
elif 'G1' in file_name:
sct.run('mv ' + file_name + ' 3d_data/')
elif not os.path.isdir(os.path.abspath('.') + '/' + file_name):
sct.run('mv ' + file_name + ' original_data/')
os.chdir('..')
os.chdir(original_path)
if "-v" in arguments:
param.verbose = int(arguments["-v"])
tmp_dir = 'tmp_' + time.strftime("%y%m%d%H%M%S")
sct.run('mkdir ' + tmp_dir)
im1_name = "im1.nii.gz"
sct.run('cp ' + input_fname + ' ' + tmp_dir + '/' + im1_name)
if input_second_fname != '':
im2_name = 'im2.nii.gz'
sct.run('cp ' + input_second_fname + ' ' + tmp_dir + '/' + im2_name)
else:
im2_name = None
os.chdir(tmp_dir)
# now = time.time()
input_im1 = Image(sct_gm.resample_image(im1_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to))
if im2_name is not None:
input_im2 = Image(sct_gm.resample_image(im2_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to))
else:
input_im2 = None
computation = ComputeDistances(input_im1, im2=input_im2, param=param)
res_fic = open('../' + output_fname, 'w')
res_fic.write(computation.res)
res_fic.write('\n\nInput 1: ' + input_fname)
res_fic.write('\nInput 2: ' + input_second_fname)
res_fic.close()
#TODO change back the orientatin of the thinned image
if param.thinning:
sct.run('cp ' + computation.thinning1.thinned_image.file_name + computation.thinning1.thinned_image.ext + ' ../' + sct.extract_fname(input_fname)[1] + '_thinned' + sct.extract_fname(input_fname)[2])
