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test_be.py
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test_be.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Sep 15 14:15:23 2016
@author: dahoiv
"""
import datetime
import nipype.interfaces.ants as ants
import os
from os.path import basename
from os.path import splitext
import image_registration
import util
def _test_be(moving_image_id, reg):
img = image_registration.img_data(moving_image_id, util.DATA_FOLDER, util.TEMP_FOLDER_PATH)
img = image_registration.pre_process(img, False)
resampled_file = img.pre_processed_filepath
name = splitext(splitext(basename(resampled_file))[0])[0] + "_bet"
img.pre_processed_filepath = util.TEMP_FOLDER_PATH + "/res/" +\
splitext(basename(resampled_file))[0] +\
'_bet.nii.gz'
reg.inputs.fixed_image = resampled_file
reg.inputs.fixed_image_mask = img.label_inv_filepath
reg.inputs.output_transform_prefix = util.TEMP_FOLDER_PATH + name
reg.inputs.output_warped_image = util.TEMP_FOLDER_PATH + name + '_betReg.nii'
transform = util.TEMP_FOLDER_PATH + name + 'InverseComposite.h5'
reg.run()
img.init_transform = transform
reg_volume = util.transform_volume(resampled_file, transform)
mult = ants.MultiplyImages()
mult.inputs.dimension = 3
mult.inputs.first_input = reg_volume
mult.inputs.second_input = image_registration.TEMPLATE_MASK
mult.inputs.output_product_image = img.pre_processed_filepath
mult.run()
util.generate_image(img.pre_processed_filepath, image_registration.TEMPLATE_VOLUME)
def test_be(moving_image_ids, reg):
start_time = datetime.datetime.now()
for moving_image_id in moving_datasets_ids:
_test_be(moving_image_id, reg)
bet_time = datetime.datetime.now() - start_time
print("\n\n\n\n -- Run time BET: ")
print(bet_time/len(moving_datasets_ids))
# pylint: disable= invalid-name
if __name__ == "__main__":
os.nice(19)
util.setup("GBM_test/", "GBM")
moving_datasets_ids = []
reg = ants.Registration()
# reg.inputs.args = "--verbose 1"
reg.inputs.collapse_output_transforms = True
reg.inputs.moving_image = image_registration.TEMPLATE_VOLUME
reg.inputs.num_threads = 8
reg.inputs.initial_moving_transform_com = True
reg.inputs.transforms = ['Rigid', 'Affine']
reg.inputs.metric = ['MI', 'MI']
reg.inputs.radius_or_number_of_bins = [32, 32]
reg.inputs.metric_weight = [1, 1]
reg.inputs.convergence_window_size = [5, 5]
reg.inputs.number_of_iterations = ([[10000, 10000, 10000, 10000],
[10000, 10000, 10000, 10000]])
reg.inputs.convergence_threshold = [1.e-6]*2
reg.inputs.shrink_factors = [[9, 5, 3, 1], [9, 5, 3, 1]]
reg.inputs.smoothing_sigmas = [[8, 4, 1, 0], [8, 4, 1, 0]]
reg.inputs.transform_parameters = [(0.25,), (0.25,)]
reg.inputs.sigma_units = ['vox']*2
reg.inputs.use_estimate_learning_rate_once = [True, True]
reg.inputs.write_composite_transform = True
reg.output_inverse_warped_image = True
# test 0
util.setup("GBM_test_0/", "GBM")
os.makedirs(util.TEMP_FOLDER_PATH)
os.makedirs(util.TEMP_FOLDER_PATH + "/res")
image_registration.prepare_template(image_registration.TEMPLATE_VOLUME,
image_registration.TEMPLATE_MASK)
test_be(moving_datasets_ids, reg)
# test 1
util.setup("GBM_test_1/", "GBM")
os.makedirs(util.TEMP_FOLDER_PATH)
os.makedirs(util.TEMP_FOLDER_PATH + "/res")
image_registration.prepare_template(image_registration.TEMPLATE_VOLUME,
image_registration.TEMPLATE_MASK)
reg.inputs.number_of_iterations = ([[10000, 5000, 1000, 500],
[10000, 5000, 1000, 500]])
test_be(moving_datasets_ids, reg)
# test 2
util.setup("GBM_test_2/", "GBM")
os.makedirs(util.TEMP_FOLDER_PATH)
os.makedirs(util.TEMP_FOLDER_PATH + "/res")
image_registration.prepare_template(image_registration.TEMPLATE_VOLUME,
image_registration.TEMPLATE_MASK)
reg.inputs.transform_parameters = [(0.75,), (0.75,)]
test_be(moving_datasets_ids, reg)
# test 3
util.setup("GBM_test_3/", "GBM")
os.makedirs(util.TEMP_FOLDER_PATH)
os.makedirs(util.TEMP_FOLDER_PATH + "/res")
image_registration.prepare_template(image_registration.TEMPLATE_VOLUME,
image_registration.TEMPLATE_MASK)
reg.inputs.transform_parameters = [(0.50,), (0.50,)]
test_be(moving_datasets_ids, reg)
# test 4
util.setup("GBM_test_4/", "GBM")
os.makedirs(util.TEMP_FOLDER_PATH)
os.makedirs(util.TEMP_FOLDER_PATH + "/res")
image_registration.prepare_template(image_registration.TEMPLATE_VOLUME,
image_registration.TEMPLATE_MASK)
reg.inputs.transform_parameters = [(0.25,), (0.25,)]
reg.inputs.radius_or_number_of_bins = [10, 10]
reg.inputs.metric = ['MI', 'MI']
test_be(moving_datasets_ids, reg)
# test 5
util.setup("GBM_test_5/", "GBM")
os.makedirs(util.TEMP_FOLDER_PATH)
os.makedirs(util.TEMP_FOLDER_PATH + "/res")
image_registration.prepare_template(image_registration.TEMPLATE_VOLUME,
image_registration.TEMPLATE_MASK)
reg.inputs.radius_or_number_of_bins = [32, 32]
reg.inputs.use_histogram_matching = [False, True]
test_be(moving_datasets_ids, reg)
# test 6
util.setup("GBM_test_5/", "GBM")
os.makedirs(util.TEMP_FOLDER_PATH)
os.makedirs(util.TEMP_FOLDER_PATH + "/res")
image_registration.prepare_template(image_registration.TEMPLATE_VOLUME,
image_registration.TEMPLATE_MASK)
reg.inputs.radius_or_number_of_bins = [32, 32, 5]
reg.inputs.convergence_window_size = [5, 5, 5]
reg.inputs.number_of_iterations = ([[10000, 5000, 1000, 500],
[10000, 5000, 1000, 500],
[100, 75, 75, 75]])
reg.inputs.shrink_factors = [[9, 5, 3, 1], [9, 5, 3, 1], [5, 3, 2, 1]]
reg.inputs.smoothing_sigmas = [[8, 4, 1, 0], [8, 4, 1, 0], [4, 2, 1, 0]]
reg.inputs.sigma_units = ['vox']*3
reg.inputs.transform_parameters = [(0.25,),
(0.25,),
(0.15, 3.0, 0.0)]
reg.inputs.use_histogram_matching = [False, False, True]
test_be(moving_datasets_ids, reg)