def evaluate(model_path, idx):
'''
Evaluate performance of single CT slice
:model_path: path of evaluated model
:idx: index of evaluated patient
'''
model = UNet(input_shape=(512, 512, 1))
model.compile(optimizer=Adam(lr=1e-4),
loss=focal_tversky_loss,
metrics=[dice_coef])
model.load_weights(model_path)
# reading original images
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(data_path + 'volume/' +
str(idx) + '/')
reader.SetFileNames(dicom_names)
images = reader.Execute()
images = sitk.GetArrayFromImage(images) # images.shape: (slices, 512, 512)
# HU values are restricted to lung window settings [-1100, 300]
# namely: window width: 1400, window level: -400
images[images < -1100] = -1100
images[images > 300] = 300
# normalization
max_value, min_value = images.max(), images.min()
images = images / (max_value - min_value)
# reading ground truth labels
labels = sitk.ReadImage(data_path + 'segmentation/' + str(idx) + '.nii')
labels = sitk.GetArrayFromImage(
labels) # eval_labels.shape: (slices, 512, 512)
# (slices, 512, 512) => (slices, 512, 512, 1)
images = np.expand_dims(images, axis=-1)
labels = np.expand_dims(labels, axis=-1)
results = model.evaluate(images, labels, batch_size=1)
for i, metric in enumerate(model.metrics_names):
print(metric, ':', results[i])
architecture = sys.argv[2]
if architecture == '--UNet':
UNet = UNet()
UNet.summary()
elif architecture == '--UNet++':
UNetPP = UNetPP()
UNetPP.summary()
else:
raise Exception(
"You have passed an invalid argument.Check the documentation for the allowed arguments."
)
elif command == "--Evaluate":
architecture = sys.argv[2]
if architecture == '--UNet':
UNet = UNet()
UNet.evaluate()
elif architecture == '--UNet++':
UNetPP = UNetPP()
UNetPP.evaluate()
else:
raise Exception(
"You have passed an invalid argument.Check the documentation for the allowed arguments."
)
else:
raise Exception(
"You have passed an invalid argument.Check the documentation for the allowed arguments."
)