def get_input(self, moving_momentum_path_list, moving_name,
init_weight_path_list):
"""
each line include the path of moving, the path of label(None if not exists)
:return:
"""
fr_sitk = lambda x: torch.Tensor(
sitk.GetArrayFromImage(sitk.ReadImage(x))).cuda()
moving = fr_sitk(moving_momentum_path_list[0])[None][None]
l_moving = None
if moving_momentum_path_list[1] is not None:
l_moving = fr_sitk(moving_momentum_path_list[1])[None][None]
if moving_name is None:
moving_name = get_file_name(moving_momentum_path_list[0])
if self.resize_output != [-1., -1, -1]:
moving, _ = resample_image(moving, [1, 1, 1],
desiredSize=[1, 1] + self.resize_output,
spline_order=1,
zero_boundary=True)
if moving_momentum_path_list[1] is not None:
l_moving, _ = resample_image(l_moving, [1, 1, 1],
desiredSize=[1, 1] +
self.resize_output,
spline_order=0,
zero_boundary=True)
return moving, l_moving, moving_name
def compute_warped_image_label(img_label_txt_pth,phi_pth,phi_type, saving_pth):
img_label_pth_list = read_txt_into_list(img_label_txt_pth)
phi_pth_list = glob(os.path.join(phi_pth,phi_type))
f = lambda pth: sitk.GetArrayFromImage(sitk.ReadImage(pth))
img_list = [f(pth[0]) for pth in img_label_pth_list]
label_list = [f(pth[1]) for pth in img_label_pth_list]
num_img = len(img_list)
for i in range(num_img):
fname = get_file_name(img_label_pth_list[i][0])
img = torch.Tensor(img_list[i][None][None])
label = torch.Tensor(label_list[i][None][None])
f_phi = lambda x: get_file_name(x).find(fname)==0
phi_sub_list = list(filter(f_phi, phi_pth_list))
num_aug = len(phi_sub_list)
phi_list = [f(pth) for pth in phi_sub_list]
img = img.repeat(num_aug,1,1,1,1)
label = label.repeat(num_aug,1,1,1,1)
phi = np.stack(phi_list,0)
phi = np.transpose(phi,(0,4,3,2,1))
phi = torch.Tensor(phi)
sz = np.array(img.shape[2:])
spacing = 1./(sz-1)
phi, _ = resample_image(phi,spacing,[1,3]+list(img.shape[2:]))
warped_img = compute_warped_image_multiNC(img,phi,spacing,spline_order=1,zero_boundary=True)
warped_label = compute_warped_image_multiNC(label,phi,spacing,spline_order=0,zero_boundary=True)
save_image_with_given_reference(warped_img,[img_label_pth_list[i][0]]*num_aug,saving_pth,[get_file_name(pth).replace("_phi","")+'_warped' for pth in phi_sub_list])
save_image_with_given_reference(warped_label,[img_label_pth_list[i][0]]*num_aug,saving_pth,[get_file_name(pth).replace("_phi","")+'_label' for pth in phi_sub_list])
def get_input(self, moving_momentum_path_list, fname_list,
init_weight_path_list):
""" each line include the path of moving, the path of label (None if not exist), path of momentum1, momentum2...."""
fr_sitk = lambda x: torch.Tensor(
sitk.GetArrayFromImage(sitk.ReadImage(x))).cuda()
moving = fr_sitk(moving_momentum_path_list[0])[None][None]
l_moving = None
if moving_momentum_path_list[1] is not None:
l_moving = fr_sitk(moving_momentum_path_list[1])[None][None]
momentum_list = [((fr_sitk(path)).permute(3, 2, 1, 0))[None]
for path in moving_momentum_path_list[2:]]
if init_weight_path_list is not None:
init_weight_list = [[
fr_sitk(path) for path in init_weight_path_list
]]
else:
init_weight_list = None
if fname_list is None:
moving_name = get_file_name(moving_momentum_path_list[0])
target_name_list = [
get_file_name(path) for path in moving_momentum_path_list[2:]
]
target_name_list = [
fname.replace("_0000_Momentum", '')
for fname in target_name_list
]
else:
moving_name = fname_list[0]
target_name_list = fname_list[1:]
if self.resize_output != [-1., -1, -1]:
moving, _ = resample_image(moving, [1, 1, 1],
desiredSize=[1, 1] + self.resize_output,
spline_order=1,
zero_boundary=True)
if moving_momentum_path_list[1] is not None:
l_moving, _ = resample_image(l_moving, [1, 1, 1],
desiredSize=[1, 1] +
self.resize_output,
spline_order=0,
zero_boundary=True)
return moving, l_moving, momentum_list, init_weight_list, moving_name, target_name_list
def get_input(self, moving_momentum_path_list, fname_list,
init_weight_path_list):
"""
each line includes path of moving, path of moving label(None if not exists), path of mom_1,...mom_m, affine_1....affine_m
"""
fr_sitk = lambda x: torch.Tensor(
sitk.GetArrayFromImage(sitk.ReadImage(x))).cuda()
moving = fr_sitk(moving_momentum_path_list[0])[None][None]
l_moving = None
if moving_momentum_path_list[1] is not None:
l_moving = fr_sitk(moving_momentum_path_list[1])[None][None]
num_m = int((len(moving_momentum_path_list) - 2) / 2)
momentum_list = [
fr_sitk(path).permute(3, 2, 1, 0)[None]
for path in moving_momentum_path_list[2:num_m + 2]
]
#affine_list =[fr_sitk(path).permute(3,2,1,0)[None] for path in moving_momentum_path_list[num_m+2:]]
affine_forward_inverse_list = [
self.read_affine_param_and_output_map(path, moving.shape[2:])
for path in moving_momentum_path_list[num_m + 2:]
]
affine_list = [
forward_inverse[0]
for forward_inverse in affine_forward_inverse_list
]
inverse_affine_list = [
forward_inverse[1]
for forward_inverse in affine_forward_inverse_list
]
if init_weight_path_list is not None:
init_weight_list = [[
fr_sitk(path) for path in init_weight_path_list
]]
else:
init_weight_list = None
if fname_list is None:
moving_name = get_file_name(moving_momentum_path_list[0])
target_name_list = [
get_file_name(path)
for path in moving_momentum_path_list[2:num_m + 2]
]
target_name_list = [
fname.replace("_0000_Momentum", '')
for fname in target_name_list
]
else:
moving_name = fname_list[0]
target_name_list = fname_list[1:]
if self.resize_output != [-1., -1, -1]:
moving, _ = resample_image(moving, [1, 1, 1],
desiredSize=[1, 1] + self.resize_output,
spline_order=1,
zero_boundary=True)
if moving_momentum_path_list[1] is not None:
l_moving, _ = resample_image(l_moving, [1, 1, 1],
desiredSize=[1, 1] +
self.resize_output,
spline_order=0,
zero_boundary=True)
return moving, l_moving, momentum_list, init_weight_list, affine_list, inverse_affine_list, moving_name, target_name_list
def generate_single_res(self, moving, l_moving, momentum, init_weight,
initial_map, initial_inverse_map, fname, t_aug,
output_path, moving_path):
params = self.mermaid_setting
params['model']['registration_model']['forward_model']['tTo'] = t_aug
# here we assume the momentum is computed at low_resol_factor=0.5
if momentum is not None:
input_img_sz = [1, 1] + [int(sz * 2) for sz in momentum.shape[2:]]
else:
input_img_sz = list(moving.shape)
momentum_sz_low = [1, 3] + [
int(dim / self.rand_momentum_shrink_factor)
for dim in input_img_sz[2:]
]
momentum_sz = [1, 3] + [int(dim / 2) for dim in input_img_sz[2:]]
momentum = (np.random.rand(*momentum_sz_low) * 2 -
1) * self.magnitude
mom_spacing = 1. / (np.array(momentum_sz_low[2:]) - 1)
momentum = torch.Tensor(momentum).cuda()
momentum, _ = resample_image(momentum,
mom_spacing,
momentum_sz,
spline_order=1,
zero_boundary=True)
if self.resize_output != [-1, -1, -1]:
momentum_sz = [1, 3] + [int(dim / 2) for dim in self.resize_output]
mom_spacing = 1. / (np.array(momentum_sz[2:]) - 1)
momentum, _ = resample_image(momentum,
mom_spacing,
momentum_sz,
spline_order=1,
zero_boundary=True)
input_img_sz = [1, 1] + [int(sz * 2) for sz in momentum.shape[2:]]
org_spacing = 1.0 / (np.array(moving.shape[2:]) - 1)
input_spacing = 1.0 / (np.array(input_img_sz[2:]) - 1)
size_diff = not input_img_sz == list(moving.shape)
if size_diff:
input_img, _ = resample_image(moving, org_spacing, input_img_sz)
else:
input_img = moving
low_initial_map = None
low_init_inverse_map = None
if initial_map is not None:
low_initial_map, _ = resample_image(
initial_map, input_spacing, [1, 3] + list(momentum.shape[2:]))
if initial_inverse_map is not None:
low_init_inverse_map, _ = resample_image(initial_inverse_map,
input_spacing, [1, 3] +
list(momentum.shape[2:]))
individual_parameters = dict(m=momentum, local_weights=init_weight)
sz = np.array(input_img.shape)
extra_info = None
visual_param = None
res = evaluate_model(input_img,
input_img,
sz,
input_spacing,
use_map=True,
compute_inverse_map=self.compute_inverse,
map_low_res_factor=0.5,
compute_similarity_measure_at_low_res=False,
spline_order=1,
individual_parameters=individual_parameters,
shared_parameters=None,
params=params,
extra_info=extra_info,
visualize=False,
visual_param=visual_param,
given_weight=False,
init_map=initial_map,
lowres_init_map=low_initial_map,
init_inverse_map=initial_inverse_map,
lowres_init_inverse_map=low_init_inverse_map)
phi = res[1]
phi_new = phi
if size_diff:
phi_new, _ = resample_image(phi, input_spacing,
[1, 3] + list(moving.shape[2:]))
warped = compute_warped_image_multiNC(moving,
phi_new,
org_spacing,
spline_order=1,
zero_boundary=True)
if initial_inverse_map is not None and self.affine_back_to_original_postion:
# here we take zero boundary boundary which need two step image interpolation
warped = compute_warped_image_multiNC(warped,
initial_inverse_map,
org_spacing,
spline_order=1,
zero_boundary=True)
phi_new = compute_warped_image_multiNC(phi_new,
initial_inverse_map,
org_spacing,
spline_order=1)
save_image_with_given_reference(warped, [moving_path], output_path,
[fname + '_image'])
if l_moving is not None:
# we assume the label doesnt lie at the boundary
l_warped = compute_warped_image_multiNC(l_moving,
phi_new,
org_spacing,
spline_order=0,
zero_boundary=True)
save_image_with_given_reference(l_warped, [moving_path],
output_path, [fname + '_label'])
if self.save_tf_map:
save_deformation(phi_new, output_path, [fname + '_phi_map'])
if self.compute_inverse:
phi_inv = res[2]
inv_phi_new = phi_inv
if self.affine_back_to_original_postion:
print(
"Cannot compute the inverse map when affine back to the source image position"
)
return
if size_diff:
inv_phi_new, _ = resample_image(phi_inv, input_spacing,
[1, 3] +
list(moving.shape[2:]))
save_deformation(inv_phi_new, output_path,
[fname + '_inv_map'])