def make_ibis_qc():
with h5py.File(workdir + 'ibis.hdf5', 'w') as f:
f.create_dataset(
'ibis_t1',
(total_subjects, target_size[0], target_size[1], target_size[2]),
dtype='float32')
f.create_dataset('qc_label', (total_subjects, 2), dtype='float32')
dt = h5py.special_dtype(vlen=bytes)
f.create_dataset('filename', (total_subjects, ), dtype=dt)
index = 0
indices = []
labels = []
with open(label_file, 'r') as labels_csv:
qc_reader = csv.reader(labels_csv)
next(qc_reader)
for line in qc_reader:
try:
t1_filename = line[3][9:]
label = line[4]
if 'Pass' in label:
one_hot = [0.0, 1.0]
pass_fail = 1
else:
one_hot = [1.0, 0.0]
pass_fail = 0
f['qc_label'][index, :] = one_hot
t1_data = nib.load(datadir + t1_filename).get_data()
if not t1_data.shape == target_size:
# print('resizing from', t1_data.shape)
t1_data = resize_image_with_crop_or_pad(
t1_data, img_size=target_size, mode='constant')
f['ibis_t1'][index, ...] = normalise_zero_one(t1_data)
f['filename'][index] = t1_filename.split('/')[-1]
# plt.imshow(t1_data[96, ...])
# plt.axis('off')
# plt.savefig(output_dir + t1_filename[:-4] + '.png', bbox_inches='tight', cmap='gray')
indices.append(index)
labels.append(pass_fail)
index += 1
except Exception as e:
print('Error:', e)
print('Total subjects we actually have:', index + 1)
return indices, labels
def make_abide_subject(line, subject_index, input_path, f):
try:
t1_filename = line[0] + '.mnc'
# register_MINC(input_path + t1_filename, atlas, input_path + '/resampled/' + t1_filename)
one_hot = [0, 0, 0]
total_labels = 0
if len(line[2]) > 0:
label1 = int(line[2]) + 1 # -1, 0, or 1
one_hot[label1] += 1
total_labels += 1
if len(line[3]) > 0:
label2 = int(line[3]) + 1
one_hot[label2] += 1
total_labels += 1
# if len(line[4]) > 0:
# label3 = int(line[4]) + 1
# one_hot[label3] += 1
# total_labels += 1
one_hot = [one_hot[0], one_hot[1] + one_hot[2]
] # map doubtful and accept to one label
one_hot = np.multiply(one_hot, 1 / total_labels)
f['qc_label'][subject_index, :] = one_hot
# print(t1_filename, one_hot)
t1_data = nib.load(input_path + '/resampled/' + t1_filename).get_data()
if not t1_data.shape == target_size:
t1_data = resize_image_with_crop_or_pad(t1_data,
img_size=target_size,
mode='constant')
f['MRI'][subject_index, ...] = normalise_zero_one(t1_data)
f['dataset'][subject_index] = 'ABIDE'
# plt.imshow(t1_data[96, ...])
# plt.axis('off')
# plt.savefig(output_dir + t1_filename[:-4] + '.png', bbox_inches='tight', cmap='gray')
return subject_index
except Exception as e:
print('Error:', e)
return -1
def make_ds030_subject(line, subject_index, input_path, f):
try:
t1_filename = line[0] + '.nii.gz'
label = line[8]
if len(label) > 0:
t1 = nib.load(input_path + t1_filename)
atlas_image = nib.load(atlas)
t1_resampled = resample_from_to(t1, atlas_image)
t1_data = t1_resampled.get_data()
if not t1_data.shape == target_size:
print('resizing from', t1_data.shape)
t1_data = resize_image_with_crop_or_pad(t1_data,
img_size=target_size,
mode='constant')
f['MRI'][subject_index, ...] = normalise_zero_one(t1_data)
one_hot = [0, 0]
if 'ok' in label:
one_hot = [0, 1]
elif 'maybe' in label:
one_hot = [0, 1]
elif 'exclude' in label:
one_hot = [1, 0]
else:
raise Exception
f['qc_label'][subject_index, :] = one_hot
f['dataset'][subject_index] = 'ds030'
# plt.imshow(t1_data[96, ...])
# plt.axis('off')
# plt.savefig(output_dir + t1_filename[:-4] + '.png', bbox_inches='tight', cmap='gray')
except Exception as e:
print('Error:', e)
return -1
return subject_index
def make_ping_subject(line, subject_index, input_path, f):
try:
t1_filename = line[0][:-4] + '.mnc'
label = int(line[1]) # 0, 1, or 2
comment = line[2]
# register_MINC(input_path + t1_filename, atlas, input_path + '/resampled/' + t1_filename)
one_hot = [0, 0]
if label >= 1:
one_hot = [0, 1]
else:
one_hot = [1, 0]
f['qc_label'][subject_index, :] = one_hot
# print(t1_filename, one_hot)
t1_data = nib.load(input_path + '/resampled/' + t1_filename).get_data()
if not t1_data.shape == target_size:
# print('resizing from', t1_data.shape)
t1_data = resize_image_with_crop_or_pad(t1_data,
img_size=target_size,
mode='constant')
# f['comment'][subject_index] = comment
f['MRI'][subject_index, ...] = normalise_zero_one(t1_data)
f['dataset'][subject_index] = 'PING'
# plt.imshow(t1_data[96, ...])
# plt.axis('off')
# plt.savefig(output_dir + t1_filename[:-4] + '.png', bbox_inches='tight', cmap='gray')
return subject_index
except Exception as e:
print('Error:', e)
return -1
description=
"Tests an MRI image for motion and other artifacts and returns a probability of success."
)
parser.add_argument("t1image")
args, leftovers = parser.parse_known_args()
image_file = args.t1image
print('Input image file:', image_file)
# K.set_image_dim_ordering('th')
try:
img = nib.load(image_file).get_data()
img = resize_image_with_crop_or_pad(img, (168, 256, 244),
mode='constant')
img = normalise_zero_one(img)
# (x_size, y_size, z_size) = img.shape
# # print('original image size:', img.shape)
#
# y_max, z_max = 256, 224
#
# while y_size < y_max or z_size < z_max:
# img = np.pad(img, 1, 'constant')
# (x_size, y_size, z_size) = img.shape
#
# y_mid, z_mid = y_size/2, z_size/2
#
# x_slice = int(x_size/2)
# y_start, y_stop = int(y_mid-y_max/2), int(y_mid+y_max/2)
def make_ibis_qc():
total_subjects = 0
with open(label_file, 'r') as labels_csv:
qc_reader = csv.reader(labels_csv)
next(qc_reader)
for line in qc_reader:
t1_filename = line[3][9:]
try:
t1 = nib.load(datadir + t1_filename)
total_subjects += 1
except:
print('Missing', t1_filename)
with h5py.File(workdir + 'ibis_revisited.hdf5', 'w') as f:
f.create_dataset(
'ibis_t1',
(total_subjects, target_size[0], target_size[1], target_size[2]),
dtype='float32')
f.create_dataset('qc_label', (total_subjects, 2), dtype='float32')
dt = h5py.special_dtype(vlen=bytes)
f.create_dataset('filename', (total_subjects, ), dtype=dt)
index = 0
indices = []
labels = []
with open(label_file, 'r') as labels_csv:
qc_reader = csv.reader(labels_csv)
next(qc_reader)
for line in qc_reader:
try:
t1_filename = line[3][9:]
label = line[4]
if 'Pass' in label:
pass_fail = 1
else:
pass_fail = 0
f['qc_label'][index] = pass_fail
t1_data = nib.load(datadir + t1_filename).get_data()
if not t1_data.shape == target_size:
# print('resizing from', t1_data.shape)
t1_data = resize_image_with_crop_or_pad(
t1_data, img_size=target_size, mode='constant')
# linear rescaling between 0 and 1
t1_data = np.subtract(t1_data, np.min(t1_data))
t1_data = np.divide(t1_data, np.max(t1_data))
t1_data = np.float32(t1_data)
x_max, y_max, z_max = 168, 256, 224
while x_size < x_max or y_size < y_max or z_size < z_max:
t1_data = np.pad(t1_data, 1, 'constant')
(x_size, y_size, z_size) = t1_data.shape
f['ibis_t1'][index, ...] = t1_data[0:x_max, 0:y_max,
0:z_max]
f['filename'][index] = t1_filename.split('/')[-1]
# plt.imshow(t1_data[96, ...])
# plt.axis('off')
# plt.savefig(output_dir + t1_filename[:-4] + '.png', bbox_inches='tight', cmap='gray')
indices.append(index)
labels.append(pass_fail)
index += 1
except Exception as e:
print('Error:', e)
print('Total subjects we actually have:', index + 1)