def gather_file(args):
if args.momentum:
file = common.AsNPCopy(common.LoadITKField(args.files[0], ca.MEM_HOST))
else:
#file = common.AsNPCopy(common.LoadITKImage(args.files[0], ca.MEM_HOST))
file = nib.load(args.files[0]).get_data()
all_size = (len(args.files), ) + (15, 15, 15, 1, 3) #file.shape;
data = torch.zeros(all_size)
for i in range(0, len(args.files)):
if args.momentum:
cur_slice = torch.from_numpy(
common.AsNPCopy(common.LoadITKField(args.files[i],
ca.MEM_HOST)))
else:
cur_slice = nib.load(args.files[i]).get_data()
if cur_slice.size == 3375:
cur_slice = np.zeros([15, 15, 15, 1, 3])
cur_slice = torch.from_numpy(cur_slice)
data[i] = cur_slice
if args.momentum:
# transpose the dataset to fit the training format
data = data.numpy()
data = np.transpose(data, [0, 4, 1, 2, 3])
data = torch.from_numpy(data)
torch.save(data, args.output)
def predict_each_datapart(args, net, network_config, input_batch, datapart_idx, batch_size, patch_size, predict_transform_space):
moving_image = torch.load(args.moving_image_dataset[datapart_idx])
target_image = torch.load(args.target_image_dataset[datapart_idx])
optimization_momentum = torch.load(args.deformation_parameter[datapart_idx])
for slice_idx in range(0, moving_image.size()[0]):
print(slice_idx)
moving_slice = moving_image[slice_idx].numpy()
target_slice = target_image[slice_idx].numpy()
if predict_transform_space:
moving_slice = util.convert_to_registration_space(moving_slice)
target_slice = util.convert_to_registration_space(target_slice)
predicted_momentum = util.predict_momentum(moving_slice, target_slice, input_batch, batch_size, patch_size, net, predict_transform_space);
m0_reg = common.FieldFromNPArr(predicted_momentum['image_space'], ca.MEM_DEVICE);
moving_image_ca = common.ImFromNPArr(moving_slice, ca.MEM_DEVICE)
target_image_ca = common.ImFromNPArr(target_slice, ca.MEM_DEVICE)
registration_result = registration_methods.geodesic_shooting(moving_image_ca, target_image_ca, m0_reg, args.shoot_steps, ca.MEM_DEVICE, network_config)
target_inv = common.AsNPCopy(registration_result['I1_inv'])
print(target_inv.shape)
if predict_transform_space:
target_inv = util.convert_to_predict_space(target_inv)
print(target_inv.shape)
target_inv = torch.from_numpy(target_inv)
target_image[slice_idx] = target_inv
optimization_momentum[slice_idx] = optimization_momentum[slice_idx] - torch.from_numpy(predicted_momentum['prediction_space'])
torch.save(target_image, args.warped_back_target_output[datapart_idx])
torch.save(optimization_momentum, args.momentum_residual[datapart_idx])
def preprocess_image(image_pyca, histeq):
image_np = common.AsNPCopy(image_pyca)
nan_mask = np.isnan(image_np)
image_np[nan_mask] = 0
image_np /= np.amax(image_np)
# perform histogram equalization if needed
if histeq:
image_np[image_np != 0] = exposure.equalize_hist(image_np[image_np != 0])
return image_np
def intensity_normalization_histeq(args):
for i in range(0, len(args.input_images)):
image = common.LoadITKImage(args.output_images[i], ca.MEM_HOST)
grid = image.grid()
image_np = common.AsNPCopy(image)
nan_mask = np.isnan(image_np)
image_np[nan_mask] = 0
image_np /= np.amax(image_np)
# perform histogram equalization if needed
if args.histeq:
image_np[image_np != 0] = exposure.equalize_hist(
image_np[image_np != 0])
image_result = common.ImFromNPArr(image_np, ca.MEM_HOST)
image_result.setGrid(grid)
common.SaveITKImage(image_result, args.output_images[i])
def predict_image(args, moving_images, target_images, output_prefixes):
if (args.use_CPU_for_shooting):
mType = ca.MEM_HOST
else:
mType = ca.MEM_DEVICE
# load the prediction network
predict_network_config = torch.load(args.prediction_parameter)
prediction_net = create_net(args, predict_network_config);
batch_size = args.batch_size
patch_size = predict_network_config['patch_size']
input_batch = torch.zeros(batch_size, 2, patch_size, patch_size, patch_size).cuda()
# use correction network if required
if args.use_correction:
correction_network_config = torch.load(args.correction_parameter);
correction_net = create_net(args, correction_network_config);
else:
correction_net = None;
# start prediction
for i in range(0, len(moving_images)):
common.Mkdir_p(os.path.dirname(output_prefixes[i]))
if (args.affine_align):
# Perform affine registration to both moving and target image to the ICBM152 atlas space.
# Registration is done using Niftireg.
call(["reg_aladin",
"-noSym", "-speeeeed", "-ref", args.atlas ,
"-flo", moving_images[i],
"-res", output_prefixes[i]+"moving_affine.nii",
"-aff", output_prefixes[i]+'moving_affine_transform.txt'])
call(["reg_aladin",
"-noSym", "-speeeeed" ,"-ref", args.atlas ,
"-flo", target_images[i],
"-res", output_prefixes[i]+"target_affine.nii",
"-aff", output_prefixes[i]+'target_affine_transform.txt'])
moving_image = common.LoadITKImage(output_prefixes[i]+"moving_affine.nii", mType)
target_image = common.LoadITKImage(output_prefixes[i]+"target_affine.nii", mType)
else:
moving_image = common.LoadITKImage(moving_images[i], mType)
target_image = common.LoadITKImage(target_images[i], mType)
#preprocessing of the image
moving_image_np = preprocess_image(moving_image, args.histeq);
target_image_np = preprocess_image(target_image, args.histeq);
grid = moving_image.grid()
#moving_image = ca.Image3D(grid, mType)
#target_image = ca.Image3D(grid, mType)
moving_image_processed = common.ImFromNPArr(moving_image_np, mType)
target_image_processed = common.ImFromNPArr(target_image_np, mType)
moving_image.setGrid(grid)
target_image.setGrid(grid)
# Indicating whether we are using the old parameter files for the Neuroimage experiments (use .t7 files from matlab .h5 format)
predict_transform_space = False
if 'matlab_t7' in predict_network_config:
predict_transform_space = True
# run actual prediction
prediction_result = util.predict_momentum(moving_image_np, target_image_np, input_batch, batch_size, patch_size, prediction_net, predict_transform_space);
m0 = prediction_result['image_space']
#convert to registration space and perform registration
m0_reg = common.FieldFromNPArr(m0, mType);
#perform correction
if (args.use_correction):
registration_result = registration_methods.geodesic_shooting(moving_image_processed, target_image_processed, m0_reg, args.shoot_steps, mType, predict_network_config)
target_inv_np = common.AsNPCopy(registration_result['I1_inv'])
correct_transform_space = False
if 'matlab_t7' in correction_network_config:
correct_transform_space = True
correction_result = util.predict_momentum(moving_image_np, target_inv_np, input_batch, batch_size, patch_size, correction_net, correct_transform_space);
m0_correct = correction_result['image_space']
m0 += m0_correct;
m0_reg = common.FieldFromNPArr(m0, mType);
registration_result = registration_methods.geodesic_shooting(moving_image, target_image, m0_reg, args.shoot_steps, mType, predict_network_config)
#endif
write_result(registration_result, output_prefixes[i]);
def predict_image(args):
if (args.use_CPU_for_shooting):
mType = ca.MEM_HOST
else:
mType = ca.MEM_DEVICE
# load the prediction network
predict_network_config = torch.load(args.prediction_parameter)
prediction_net = create_net(args, predict_network_config)
batch_size = args.batch_size
patch_size = predict_network_config['patch_size']
input_batch = torch.zeros(batch_size, 2, patch_size, patch_size,
patch_size).cuda()
# start prediction
for i in range(0, len(args.moving_image)):
common.Mkdir_p(os.path.dirname(args.output_prefix[i]))
if (args.affine_align):
# Perform affine registration to both moving and target image to the ICBM152 atlas space.
# Registration is done using Niftireg.
call([
"reg_aladin", "-noSym", "-speeeeed", "-ref", args.atlas,
"-flo", args.moving_image[i], "-res",
args.output_prefix[i] + "moving_affine.nii", "-aff",
args.output_prefix[i] + 'moving_affine_transform.txt'
])
call([
"reg_aladin", "-noSym", "-speeeeed", "-ref", args.atlas,
"-flo", args.target_image[i], "-res",
args.output_prefix[i] + "target_affine.nii", "-aff",
args.output_prefix[i] + 'target_affine_transform.txt'
])
moving_image = common.LoadITKImage(
args.output_prefix[i] + "moving_affine.nii", mType)
target_image = common.LoadITKImage(
args.output_prefix[i] + "target_affine.nii", mType)
else:
moving_image = common.LoadITKImage(args.moving_image[i], mType)
target_image = common.LoadITKImage(args.target_image[i], mType)
#preprocessing of the image
moving_image_np = preprocess_image(moving_image, args.histeq)
target_image_np = preprocess_image(target_image, args.histeq)
grid = moving_image.grid()
moving_image_processed = common.ImFromNPArr(moving_image_np, mType)
target_image_processed = common.ImFromNPArr(target_image_np, mType)
moving_image.setGrid(grid)
target_image.setGrid(grid)
predict_transform_space = False
if 'matlab_t7' in predict_network_config:
predict_transform_space = True
# run actual prediction
prediction_result = util.predict_momentum(moving_image_np,
target_image_np, input_batch,
batch_size, patch_size,
prediction_net,
predict_transform_space)
m0 = prediction_result['image_space']
m0_reg = common.FieldFromNPArr(prediction_result['image_space'], mType)
registration_result = registration_methods.geodesic_shooting(
moving_image_processed, target_image_processed, m0_reg,
args.shoot_steps, mType, predict_network_config)
phi = common.AsNPCopy(registration_result['phiinv'])
phi_square = np.power(phi, 2)
for sample_iter in range(1, args.samples):
print(sample_iter)
prediction_result = util.predict_momentum(
moving_image_np, target_image_np, input_batch, batch_size,
patch_size, prediction_net, predict_transform_space)
m0 += prediction_result['image_space']
m0_reg = common.FieldFromNPArr(prediction_result['image_space'],
mType)
registration_result = registration_methods.geodesic_shooting(
moving_image_processed, target_image_processed, m0_reg,
args.shoot_steps, mType, predict_network_config)
phi += common.AsNPCopy(registration_result['phiinv'])
phi_square += np.power(
common.AsNPCopy(registration_result['phiinv']), 2)
m0_mean = np.divide(m0, args.samples)
m0_reg = common.FieldFromNPArr(m0_mean, mType)
registration_result = registration_methods.geodesic_shooting(
moving_image_processed, target_image_processed, m0_reg,
args.shoot_steps, mType, predict_network_config)
phi_mean = registration_result['phiinv']
phi_var = np.divide(phi_square, args.samples) - np.power(
np.divide(phi, args.samples), 2)
#save result
common.SaveITKImage(registration_result['I1'],
args.output_prefix[i] + "I1.mhd")
common.SaveITKField(phi_mean,
args.output_prefix[i] + "phiinv_mean.mhd")
common.SaveITKField(common.FieldFromNPArr(phi_var, mType),
args.output_prefix[i] + "phiinv_var.mhd")