# Create a pixel value normalization Subfunction to scale between 0-255
sf_normalize = Normalization(mode="grayscale")
# Create a resampling Subfunction to voxel spacing 1.58 x 1.58 x 2.70
sf_resample = Resampling((1.58, 1.58, 2.70))
# Create a pixel value normalization Subfunction for z-score scaling
sf_zscore = Normalization(mode="z-score")
# Assemble Subfunction classes into a list
sf = [sf_clipping, sf_normalize, sf_resample, sf_zscore]
# Create and configure the Preprocessor class
pp = Preprocessor(data_io, data_aug=data_aug, batch_size=2, subfunctions=sf,
prepare_subfunctions=True, prepare_batches=False,
analysis="fullimage", patch_shape=(160, 160, 80))
# Adjust the patch overlap for predictions
pp.patchwise_overlap = (80, 80, 40)
# Initialize Keras Data Generator for generating batches
from miscnn.neural_network.data_generator import DataGenerator
dataGen = DataGenerator(sample_list, pp, training=False, validation=False, shuffle=False)
x = []
y = []
z = []
for batch in dataGen:
print("Batch:", batch.shape)
x.append(batch.shape[1])
y.append(batch.shape[2])
z.append(batch.shape[3])
def run(self):
# Create sample list for miscnn
util.create_sample_list(self.input_dir)
# Initialize Data IO Interface for NIfTI data
interface = NIFTI_interface(channels=1, classes=2)
# Create Data IO object to load and write samples in the file structure
data_io = Data_IO(interface,
input_path=self.input_dir,
delete_batchDir=False)
# Access all available samples in our file structure
sample_list = data_io.get_indiceslist()
sample_list.sort()
# Create a resampling Subfunction to voxel spacing 1.58 x 1.58 x 2.70
sf_resample = Resampling((1.58, 1.58, 2.70))
# Create a pixel value normalization Subfunction for z-score scaling
sf_zscore = Normalization(mode="z-score")
# Create a pixel value normalization Subfunction to scale between 0-255
sf_normalize = Normalization(mode="grayscale")
# Assemble Subfunction classes into a list
sf = [sf_normalize, sf_resample, sf_zscore]
# Create and configure the Preprocessor class
pp = Preprocessor(data_io,
batch_size=2,
subfunctions=sf,
prepare_subfunctions=True,
prepare_batches=False,
analysis="patchwise-crop",
patch_shape=(160, 160, 80))
# Adjust the patch overlap for predictions
pp.patchwise_overlap = (80, 80, 30)
# Initialize the Architecture
unet_standard = Architecture(depth=4,
activation="softmax",
batch_normalization=True)
# Create the Neural Network model
model = Neural_Network(
preprocessor=pp,
architecture=unet_standard,
loss=tversky_crossentropy,
metrics=[tversky_loss, dice_soft, dice_crossentropy],
batch_queue_size=3,
workers=1,
learninig_rate=0.001)
# Load best model weights during fitting
model.load(f'{self.model_dir}{self.model_name}.hdf5')
# Obtain training and validation data set ----- CHANGE BASED ON PRED/TRAIN
images, _ = load_disk2fold(f'{self.input_dir}sample_list.json')
print('\n\nRunning automatic segmentation on samples...\n')
print(f'Segmenting images: {images}')
# Compute predictions
self.predictions = model.predict(images)
# Delete folder created by miscnn
shutil.rmtree('batches/')
