def _run_single_scan(self, idx):
in_img = ScanWrapper(self._in_data_folder.get_file_path(idx))
in_mask = ScanWrapper(self._in_mask_folder_obj.get_file_path(idx))
in_img_data = in_img.get_data()
in_mask_data = in_mask.get_data()
mask_data = np.zeros(in_mask_data.shape, dtype=int)
for lung_label in self._lung_label_list:
mask_match_map = (in_mask_data == lung_label).astype(int)
print(f'lung label: {lung_label}')
print(f'match_map shape: {mask_match_map.shape}')
print(f'num posi voxels: {np.sum(mask_match_map)}')
mask_data += mask_match_map
mask_data = ~mask_data.astype('bool')
print(f'Final mask: {np.sum(mask_data)}')
in_img_data_with_mask = np.ma.array(in_img_data, mask=mask_data.astype('bool'))
mean_in_mask = in_img_data_with_mask.mean()
file_name = self._in_data_folder.get_file_name(idx)
result_dict = {
'file_name': file_name,
'mean': mean_in_mask
}
print(result_dict)
return result_dict
def clip_plot_3_view(self, out_png_folder):
in_img_obj = ScanWrapper(self._in_img_path)
in_mask_obj = ScanWrapper(self._in_mask_path)
in_img_data = in_img_obj.get_data()
in_mask_data = in_mask_obj.get_data()
masked_img_data = np.zeros(in_img_data.shape, dtype=float)
masked_img_data.fill(np.nan)
masked_img_data[in_mask_data == 1] = in_img_data[in_mask_data == 1]
def clip_plot(self, out_png_folder):
in_img_obj = ScanWrapper(self._in_img_path)
in_back_obj = ScanWrapper(self._in_back_img_path)
in_img_data = in_img_obj.get_data()
in_back_data = in_back_obj.get_data()
masked_img_data = None
masked_back_data = None
if self._in_mask_path is not None:
in_mask_obj = ScanWrapper(self._in_mask_path)
in_mask_data = in_mask_obj.get_data()
masked_img_data = np.zeros(in_img_data.shape, dtype=float)
masked_img_data.fill(np.nan)
masked_img_data[in_mask_data == 1] = in_img_data[in_mask_data == 1]
masked_back_data = np.zeros(in_back_data.shape, dtype=float)
masked_back_data.fill(np.nan)
masked_back_data[in_mask_data == 1] = in_back_data[in_mask_data == 1]
else:
masked_img_data = in_img_data
masked_back_data = in_back_data
self._plot_stacked_view(
self._num_clip,
self._step_axial,
masked_img_data,
masked_back_data,
'axial',
out_png_folder,
1
)
self._plot_stacked_view(
self._num_clip,
self._step_sagittal,
masked_img_data,
masked_back_data,
'sagittal',
out_png_folder,
5.23438 / 2.28335
)
self._plot_stacked_view(
self._num_clip,
self._step_coronal,
masked_img_data,
masked_back_data,
'coronal',
out_png_folder,
5.23438 / 2.17388
)
def _run_single_scan(self, idx):
in_file_path = self._in_data_folder.get_file_path(idx)
in_data = ScanWrapper(in_file_path)
in_img = in_data.get_data()
# self._data_matrix[idx, :] = in_img.reshape(in_data.get_number_voxel())
self._data_matrix[idx, :] = convert_3d_2_flat(in_img)
def plot_pc(self, out_png):
fig = plt.figure(figsize=(self._num_show_pc * 20,
self._num_clip * self._num_view * 15))
gs = gridspec.GridSpec(1, self._num_show_pc)
sub_gs_list = []
for idx_pc in range(self._num_show_pc):
sub_gs = gs[idx_pc].subgridspec(self._num_clip * self._num_view, 1)
sub_gs_list.append(sub_gs)
for idx_pc in range(self._num_show_pc):
img_data_obj = ScanWrapper(
self._pc_folder_obj.get_file_path(idx_pc))
img_data = img_data_obj.get_data()
img_name = os.path.basename(
self._pc_folder_obj.get_file_path(idx_pc)).replace(
'.nii.gz', '')
logger.info(
f'Reading image {self._pc_folder_obj.get_file_path(idx_pc)}')
self._plot_one_pc(img_data, sub_gs_list[idx_pc], f'{img_name}')
# out_eps = out_png.replace('.png', '.eps')
logger.info(f'Save fig to {out_png}')
# plt.savefig(out_eps, bbox_inches='tight', pad_inches=0, dpi=self._out_dpi, format='pdf')
plt.savefig(out_png,
bbox_inches='tight',
pad_inches=0,
dpi=self._out_dpi)
plt.close(fig=fig)
def _run_single_scan(self, idx):
in_img = ScanWrapper(self._in_data_folder.get_file_path(idx))
out_mask_path = self._out_folder.get_file_path(idx)
in_img_data = in_img.get_data()
non_nan_mask = in_img_data == in_img_data
logger.info(f'Save non-nan mask to {out_mask_path}')
in_img.save_scan_same_space(out_mask_path, non_nan_mask.astype(int))
def clip_plot(self, out_png_folder):
in_int_obj = ScanWrapper(self._in_int_path)
in_jac_obj = ScanWrapper(self._in_jac_path)
in_att_obj = ScanWrapper(self._in_att_path)
in_int_data = in_int_obj.get_data()
in_jac_data = in_jac_obj.get_data()
in_att_data = in_att_obj.get_data()
fig, axs = plt.subplots(1, 3, constrained_layout=True, figsize=(30, 30))
# plt.axis('off')
for ax in axs:
ax.axis('off')
self._plot_view(
self._num_clip,
self._step_axial,
in_int_data, in_jac_data, in_att_data,
'axial', 1, axs[0]
)
self._plot_view(
self._num_clip,
self._step_coronal,
in_int_data, in_jac_data, in_att_data,
'coronal', 5.23438 / 2.17388, axs[1]
)
self._plot_view(
self._num_clip,
self._step_sagittal,
in_int_data, in_jac_data, in_att_data,
'sagittal', 5.23438 / 2.28335, axs[2]
)
out_root_folder = os.path.join(out_png_folder, 'stacked')
mkdir_p(out_root_folder)
out_png_path = os.path.join(out_root_folder, f'{self._in_img_file_name}.png')
print(f'Save overlay png to {out_png_path}')
plt.savefig(out_png_path, bbox_inches='tight', pad_inches=0)
plt.close()
def _run_chunk(self, chunk_list):
result_list = []
im_shape = self._ref_img.get_shape()
sum_image_union = np.zeros(im_shape)
region_mask_count_image = np.zeros(im_shape)
for idx in chunk_list:
self._in_data_folder.print_idx(idx)
img_obj = ScanWrapper(self._in_data_folder.get_file_path(idx))
mask_obj = ScanWrapper(
self._in_region_mask_folder.get_file_path(idx))
sum_image_union += img_obj.get_data()
region_mask_count_image += mask_obj.get_data()
result = {
'sum_image': sum_image_union,
'region_count': region_mask_count_image
}
result_list.append(result)
return result_list
def _plot_pc_one_view(self, idx_view, out_png_path):
view_config_list = self._get_view_config()
clip_plane = view_config_list[idx_view]['clip plane']
num_files = self._pc_folder_obj.num_files()
num_lines = int(num_files / self._num_show_pc)
fig = plt.figure(figsize=(self._num_show_pc * 15, num_lines * 12))
gs = gridspec.GridSpec(num_lines, self._num_show_pc)
gs.update(wspace=0.025, hspace=0.025)
for idx_row in range(num_lines):
# if idx_row != 0:
# continue
for idx_column in range(self._num_show_pc):
# if idx_column != 0:
# continue
idx_file = idx_column + self._num_show_pc * idx_row
logger.info(
f'Plot scan {self._pc_folder_obj.get_file_path(idx_file)}')
img_data_obj = ScanWrapper(
self._pc_folder_obj.get_file_path(idx_file))
img_data = img_data_obj.get_data()
range_norm = np.max([np.min(img_data), np.max(img_data)])
# vmin = - 0.5 * range_norm
# vmax = 0.5 * range_norm
vmin = -0.0015
vmax = 0.0015
clip = self._clip_image(img_data, clip_plane, 0)
ax = plt.subplot(gs[idx_row, idx_column])
plt.axis('off')
im = plt.imshow(clip,
interpolation='none',
cmap=self._cm,
norm=colors.Normalize(vmin=vmin, vmax=vmax))
# divider = make_axes_locatable(ax)
# cax = divider.append_axes("right", size="5%", pad=0.05)
#
# cb = plt.colorbar(im, cax=cax)
# # cb.set_label('Intensity of eigen image')
# cb.ax.tick_params(labelsize=self._sub_title_font_size / 2)
logger.info(f'Save plot to {out_png_path}')
plt.savefig(out_png_path,
bbox_inches='tight',
pad_inches=0.1,
dpi=self._out_dpi)
plt.close(fig=fig)
def _run_single_scan(self, idx):
in_data_path = self._in_data_folder.get_file_path(idx)
in_affine_mat_path = self._in_affine_mat_folder.get_file_path(
idx).replace('.nii.gz', '.txt')
out_file_path = self._out_data_folder.get_file_path(idx)
in_image = ScanWrapper(in_data_path)
affine_mat = self._get_affine_matrix(in_affine_mat_path)
new_log_jacobian_det = in_image.get_data() + np.log(
np.linalg.det(affine_mat))
self._ref_img.save_scan_same_space(out_file_path, new_log_jacobian_det)
def get_average_map(file_list, save_path):
first_img = ScanWrapper(file_list[0])
im_shape = first_img.get_shape()
sum_map = np.zeros(im_shape, dtype=float)
for idx_image in range(len(file_list)):
img = ScanWrapper(file_list[idx_image])
img_data = img.get_data()
print(f'Adding {file_list[idx_image]} ({idx_image} / {len(file_list)})')
print(f'Max intensity {np.max(img_data)}')
sum_map += img_data
average_map = sum_map / float(len(file_list))
print(f'Average map max int {np.max(average_map)}')
first_img.save_scan_same_space(save_path, average_map)
class OverlayMaskPNG(AbstractParallelRoutine):
def __init__(self, config, in_folder, mask_img, out_png_folder, file_list_txt):
super().__init__(config, in_folder, file_list_txt)
self._mask_img = ScanWrapper(mask_img)
self._out_png_folder = DataFolder.get_data_folder_obj(config, out_png_folder, data_list_txt=file_list_txt)
self._out_png_folder.change_suffix('.png')
self._vmax = 500
self._vmin = -1000
def _run_single_scan(self, idx):
in_img_path = self._in_data_folder.get_file_path(idx)
in_img = ScanWrapper(in_img_path)
slice_in_img = self._clip_nifti(in_img.get_data())
slice_mask_img = self._clip_nifti(self._mask_img.get_data())
plt.figure(figsize=(15, 15))
plt.axis('off')
clip_x_nii_rescale = exposure.rescale_intensity(slice_in_img, in_range=(self._vmin, self._vmax), out_range=(0, 1))
clip_x_nii_rgb = color.gray2rgb(clip_x_nii_rescale)
plt.imshow(clip_x_nii_rgb, alpha=0.8)
plt.imshow(slice_mask_img,
interpolation='none',
cmap='jet',
norm=colors.Normalize(vmin=0, vmax=1),
alpha=0.3)
out_png_path = self._out_png_folder.get_file_path(idx)
print(f'Saving image to {out_png_path}')
plt.savefig(out_png_path, bbox_inches='tight', pad_inches=0)
@staticmethod
def _clip_nifti(im_data, offset=0):
im_shape = im_data.shape
clip_x = im_data[int(im_shape[0] / 2) - 1 + offset, :, :]
clip_x = np.flip(clip_x, 0)
clip_x = np.rot90(clip_x)
return clip_x
def _run_single_scan(self, idx):
in_img_path = self._in_data_folder.get_file_path(idx)
in_img = ScanWrapper(in_img_path)
slice_in_img = self._clip_nifti(in_img.get_data())
slice_mask_img = self._clip_nifti(self._mask_img.get_data())
plt.figure(figsize=(15, 15))
plt.axis('off')
clip_x_nii_rescale = exposure.rescale_intensity(slice_in_img, in_range=(self._vmin, self._vmax), out_range=(0, 1))
clip_x_nii_rgb = color.gray2rgb(clip_x_nii_rescale)
plt.imshow(clip_x_nii_rgb, alpha=0.8)
plt.imshow(slice_mask_img,
interpolation='none',
cmap='jet',
norm=colors.Normalize(vmin=0, vmax=1),
alpha=0.3)
out_png_path = self._out_png_folder.get_file_path(idx)
print(f'Saving image to {out_png_path}')
plt.savefig(out_png_path, bbox_inches='tight', pad_inches=0)
class PreprocessAverageImputation(AbstractParallelRoutine):
def __init__(self,
config,
in_folder,
out_folder,
average_img,
file_list_txt=None):
super().__init__(config, in_folder, file_list_txt=file_list_txt)
self._out_data_folder = DataFolder.get_data_folder_obj(
config, out_folder, data_list_txt=file_list_txt)
mkdir_p(out_folder)
self._average_img = ScanWrapper(average_img)
def _run_single_scan(self, idx):
in_file_path = self._in_data_folder.get_file_path(idx)
out_file_path = self._out_data_folder.get_file_path(idx)
in_img = ScanWrapper(in_file_path).get_data()
average_img = self._average_img.get_data()
np.copyto(in_img, average_img, where=(in_img != in_img))
np.copyto(in_img, 0, where=(in_img != in_img))
self._average_img.save_scan_same_space(out_file_path, in_img)
class AverageScans:
def __init__(self,
config,
in_folder=None,
data_file_txt=None,
in_data_folder_obj=None):
self._data_folder = None
if in_data_folder_obj is None:
self._data_folder = DataFolder(in_folder, data_file_txt)
else:
self._data_folder = in_data_folder_obj
self._standard_ref = ScanWrapper(self._data_folder.get_first_path())
self._num_processes = config['num_processes']
def get_average_image_union(self, save_path):
im_shape = self._get_std_shape()
average_union = np.zeros(im_shape)
average_union.fill(np.nan)
non_null_mask_count_image = np.zeros(im_shape)
chunk_list = self._data_folder.get_chunks_list(self._num_processes)
pool = Pool(processes=self._num_processes)
print('Average in union')
print('Step.1 Summation')
image_average_union_result_list = [
pool.apply_async(self._sum_images_union, (file_idx_chunk, ))
for file_idx_chunk in chunk_list
]
for thread_idx in range(len(image_average_union_result_list)):
result = image_average_union_result_list[thread_idx]
result.wait()
print(
f'Thread with idx {thread_idx} / {len(image_average_union_result_list)} is completed'
)
print('Adding to averaged_image...')
averaged_image_chunk = result.get()
average_union = self._add_image_union(average_union,
averaged_image_chunk)
print('Done.')
print('Step.2 Non-nan counter')
non_null_mask_count_result = [
pool.apply_async(self._sum_non_null_count, (file_idx_chunk, ))
for file_idx_chunk in chunk_list
]
for thread_idx in range(len(non_null_mask_count_result)):
result = non_null_mask_count_result[thread_idx]
result.wait()
print(
f'Thread with idx {thread_idx} / {len(non_null_mask_count_result)} is completed'
)
print('Adding to averaged_image...')
averaged_image_chunk = result.get()
non_null_mask_count_image = np.add(non_null_mask_count_image,
averaged_image_chunk)
print('Done.')
average_union = np.divide(average_union,
non_null_mask_count_image,
out=average_union,
where=non_null_mask_count_image > 0)
self._standard_ref.save_scan_same_space(save_path, average_union)
print('Done.')
def _sum_images_union(self, chunk_list):
print('Sum images, union non-null region. Loading images...')
im_shape = self._get_std_shape()
sum_image = np.zeros(im_shape)
sum_image.fill(np.nan)
for id_file in chunk_list:
file_path = self._data_folder.get_file_path(id_file)
self._data_folder.print_idx(id_file)
im = nib.load(file_path)
im_data = im.get_data()
sum_image = self._add_image_union(sum_image, im_data)
return sum_image
def _sum_non_null_count(self, chunk_list):
print('Count non-null per voxel. Loading images...')
im_shape = self._get_std_shape()
sum_image = np.zeros(im_shape)
for id_file in chunk_list:
file_path = self._data_folder.get_file_path(id_file)
self._data_folder.print_idx(id_file)
im = nib.load(file_path)
im_data = im.get_data()
sum_image = np.add(sum_image,
1,
out=sum_image,
where=np.logical_not(np.isnan(im_data)))
return sum_image
def _get_std_shape(self):
return self._standard_ref.get_data().shape
@staticmethod
def _add_image_inter(image1, image2):
return np.add(image1,
image2,
out=np.full_like(image1, np.nan),
where=np.logical_not(
np.logical_or(np.isnan(image1), np.isnan(image2))))
@staticmethod
def _add_image_union(image1, image2):
add_image = np.full_like(image1, np.nan)
add_image[np.logical_not(
np.logical_and(np.isnan(image1), np.isnan(image2)))] = 0
add_image = np.add(add_image,
image1,
out=add_image,
where=np.logical_not(np.isnan(image1)))
add_image = np.add(add_image,
image2,
out=add_image,
where=np.logical_not(np.isnan(image2)))
return add_image
@staticmethod
def sum_non_null_count(file_list, in_folder):
print('Count non-null per voxel. Loading images...')
im_temp = nib.load(os.path.join(in_folder, file_list[0]))
im_temp_data = im_temp.get_data()
sum_image = np.zeros_like(im_temp_data)
for id_file in range(len(file_list)):
file_name = file_list[id_file]
print('%s (%d/%d)' % (file_name, id_file, len(file_list)))
file_path = os.path.join(in_folder, file_name)
im = nib.load(file_path)
im_data = im.get_data()
sum_image = np.add(sum_image,
1,
out=sum_image,
where=np.logical_not(np.isnan(im_data)))
return sum_image
def _calc_nmse(ref_img: ScanWrapper, in_img: ScanWrapper):
mse_val = metrics.normalized_root_mse(image_true=ref_img.get_data(),
image_test=in_img.get_data())
return mse_val
def _run_single_scan(self, idx):
in_img = ScanWrapper(self._in_data_folder.get_file_path(idx))
return in_img.get_data()