model = antspynet.create_deep_back_projection_network_model_3d(
(*input_image_list[0].shape, 1),
number_of_outputs=1,
number_of_base_filters=64,
number_of_feature_filters=256,
number_of_back_projection_stages=7,
convolution_kernel_size=(3, 3, 3),
strides=(2, 2, 2),
number_of_loss_functions=1)
print("Loading weights file")
start_time = time.time()
weights_file_name = "./mriSuperResolutionWeights.h5"
if not os.path.exists(weights_file_name):
weights_file_name = antspynet.get_pretrained_network(
"mriSuperResolution", weights_file_name)
model.load_weights(weights_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
number_of_image_volumes = len(input_image_list)
output_image_list = list()
for i in range(number_of_image_volumes):
print("Applying super resolution to image", i, "of",
number_of_image_volumes)
start_time = time.time()
input_image = ants.iMath(input_image_list[i], "TruncateIntensity", 0.0001,
start_time = time.time()
# image_resampled = ants.resample_image(image_standard, shape_initial_stage, True, 0)
image_resampled = image_standard
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
# Build model and load weights for first pass
print(" Initial step 5: load weights.")
start_time = time.time()
model_initial_stage = antspynet.create_hippmapp3r_unet_model_3d(
(*shape_initial_stage, 1), True)
weights_file_name = "./hippMapp3rInitialWeights.h5"
if not os.path.exists(weights_file_name):
weights_file_name = antspynet.get_pretrained_network(
"hippMapp3rInitial", weights_file_name)
model_initial_stage.load_weights(weights_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
# Create initial segmentation image
print(" Initial step 6: prediction.")
start_time = time.time()
data_initial_stage = image_resampled.numpy()
data_initial_stage = np.expand_dims(data_initial_stage, 0)
data_initial_stage = np.expand_dims(data_initial_stage, -1)
prediction_initial_stage = np.squeeze(
model_initial_stage.predict(data_initial_stage))
unet_model = antspynet.create_unet_model_3d(
(*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4,
number_of_filters_at_base_layer=16,
dropout_rate=0.0,
convolution_kernel_size=(7, 7, 5),
deconvolution_kernel_size=(7, 7, 5))
print("Loading weights file")
start_time = time.time()
weights_file_name = "./lungSegmentationWeights.h5"
if not os.path.exists(weights_file_name):
weights_file_name = antspynet.get_pretrained_network(
"protonLungMri", weights_file_name)
unet_model.load_weights(weights_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
start_time_total = time.time()
print("Reading ", input_file_name)
start_time = time.time()
image = ants.image_read(input_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
print(" (elapsed time: ", elapsed_time, " seconds)")
resampled_image_size = reorient_template.shape
unet_model = antspynet.create_unet_model_3d( (*resampled_image_size, channel_size),
number_of_outputs = number_of_classification_labels,
number_of_layers = 4, number_of_filters_at_base_layer = 8, dropout_rate = 0.0,
convolution_kernel_size = (3, 3, 3), deconvolution_kernel_size = (2, 2, 2),
weight_decay = 1e-5 )
print( "Loading weights file" )
start_time = time.time()
weights_file_name = "./brainExtractionWeights.h5"
if not os.path.exists(weights_file_name):
weights_file_name = antspynet.get_pretrained_network("brainExtraction", weights_file_name)
unet_model.load_weights(weights_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
start_time_total = time.time()
print( "Reading ", input_file_name )
start_time = time.time()
image = ants.image_read(input_file_name)
image = (image - image.min()) / (image.max() - image.min())
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
unet_model = antspynet.create_unet_model_3d(
(*patch_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4,
number_of_filters_at_base_layer=16,
dropout_rate=0.0,
convolution_kernel_size=(3, 3, 3),
deconvolution_kernel_size=(2, 2, 2),
weight_decay=1e-5)
print("Loading weights file")
start_time = time.time()
weights_file_name = "./brainSegmentationPatchBasedWeights.h5"
if not os.path.exists(weights_file_name):
weights_file_name = antspynet.get_pretrained_network(
"brainSegmentationPatchBased", weights_file_name)
unet_model.load_weights(weights_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
# Process input
start_time_total = time.time()
print("Reading ", input_file_name)
start_time = time.time()
image = ants.image_read(input_file_name)
mask = ants.image_read(input_mask_file_name)
mask = ants.threshold_image(mask, 0.4999, 1.0001, 1, 0)
image_mean = np.mean(thresholded_nonzero)
image_sd = np.std(thresholded_nonzero)
image_standard = (image_n4 - image_mean) / image_sd
image_standard = image_standard * thresholded_mask
# Resample image
print(" Initial step 4: resample to (160, 160, 128).")
image_resampled = ants.resample_image(image_standard, shape_initial_stage,
True, 0)
# Build model and load weights for first pass
print(" Initial step 5: generate first network and download weights.")
model_initial_stage = antspynet.create_hippmapp3r_unet_model_3d(
(*shape_initial_stage, 1), True)
model_initial_stage.load_weights(
antspynet.get_pretrained_network("hippMapp3rInitial"))
# Create initial segmentation image
print(" Initial step 6: prediction and write to disk.")
data_initial_stage = image_resampled.numpy()
data_initial_stage = np.expand_dims(data_initial_stage, 0)
data_initial_stage = np.expand_dims(data_initial_stage, -1)
prediction_initial_stage = np.squeeze(
model_initial_stage.predict(data_initial_stage))
prediction_initial_stage[np.where(prediction_initial_stage >= 0.5)] = 1
prediction_initial_stage[np.where(prediction_initial_stage < 0.5)] = 0
mask_initial_stage = ants.from_numpy(prediction_initial_stage,
origin=image_resampled.origin,
spacing=image_resampled.spacing,
direction=image_resampled.direction)