def test_IOI_NIFTI_loading(self):
interface = NIFTI_interface(pattern="nifti")
sample_list = interface.initialize(self.tmp_data.name)
img, extended = interface.load_image(sample_list[0])
# Create a Sample object
sample = MIScnn_sample.Sample(sample_list[0], img, interface.channels, interface.classes, extended)
seg = interface.load_segmentation(sample_list[0])
self.assertTrue(np.array_equal(img, self.img))
self.assertTrue(np.array_equal(seg, self.seg))
def test_IOI_NIFTI_predictionhandling(self):
interface = NIFTI_interface(pattern="nifti")
sample_list = interface.initialize(self.tmp_data.name)
sample = MIScnn_sample.Sample("pred.nifti", np.asarray([0]), interface.channels, interface.classes)
sample.add_prediction(self.seg);
interface.save_prediction(sample, self.tmp_data.name)
pred = interface.load_prediction("pred.nifti", self.tmp_data.name)
self.assertTrue(np.array_equal(pred.reshape(self.seg.shape), self.seg))
def test_IOI_NIFTI_loading(self):
interface = NIFTI_interface(pattern="nifti")
sample_list = interface.initialize(self.tmp_data.name)
img = interface.load_image(sample_list[0])
seg = interface.load_segmentation(sample_list[0])
details = interface.load_details(sample_list[0])
self.assertTrue(np.array_equal(img, self.img))
self.assertTrue(np.array_equal(seg, self.seg))
def setUpClass(self):
# Create imgaging and segmentation data set
np.random.seed(1234)
self.dataset = dict()
for i in range(0, 10):
img = np.random.rand(16, 16, 16) * 256
self.img = img.astype(int)
seg = np.random.rand(16, 16, 16) * 3
self.seg = seg.astype(int)
sample = (self.img, self.seg)
self.dataset["TEST.sample_" + str(i)] = sample
# Initialize Dictionary IO Interface
# Initialize temporary directory
self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.miscnn.")
self.tmp_data = os.path.join(self.tmp_dir.name, "data")
os.mkdir(self.tmp_data)
for key, value in self.dataset.items():
write_sample(value, self.tmp_data, key)
self.dataio = Data_IO(NIFTI_interface(), self.tmp_data)
from miscnn.evaluation.cross_validation import run_fold, load_csv2fold
import os
import numpy as np
#-----------------------------------------------------#
# Tensorflow Configuration for GPU Cluster #
#-----------------------------------------------------#
# physical_devices = tf.config.list_physical_devices('GPU')
# tf.config.experimental.set_memory_growth(physical_devices[0], True)
#-----------------------------------------------------#
# Setup of MIScnn Pipeline #
#-----------------------------------------------------#
# Initialize Data IO Interface for NIfTI data
## We are using 4 classes due to [background, lung_left, lung_right, covid-19]
interface = NIFTI_interface(channels=1, classes=4)
# Create Data IO object to load and write samples in the file structure
data_io = Data_IO(interface, input_path="data", delete_batchDir=False)
# Access all available samples in our file structure
sample_list = data_io.get_indiceslist()
sample_list.sort()
# Create and configure the Data Augmentation class
data_aug = Data_Augmentation(cycles=1, scaling=True, rotations=True,
elastic_deform=True, mirror=True,
brightness=True, contrast=True, gamma=True,
gaussian_noise=True)
# Create a clipping Subfunction to the lung window of CTs (-1250 and 250)
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/')
def test_IOI_NIFTI_initialize(self):
interface = NIFTI_interface(pattern="nifti")
sample_list = interface.initialize(self.tmp_data.name)
self.assertEqual(len(sample_list), 1)
self.assertEqual(sample_list[0], "nifti")
def test_IOI_NIFTI_creation(self):
interface = NIFTI_interface()
def test_IOI_NIFTI_predictionhandling(self):
interface = NIFTI_interface(pattern="nifti")
sample_list = interface.initialize(self.tmp_data.name)
interface.save_prediction(self.seg, "pred.nifti", self.tmp_data.name)
pred = interface.load_prediction("pred.nifti", self.tmp_data.name)
self.assertTrue(np.array_equal(pred, self.seg))
