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import datetime
import os, sys
import shutil
import re
import argparse
import subprocess
import logging
import time
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir) # PYTHON > 3.3 does not allow relative referencing
import tensorflow as tf
import numpy as np
import nibabel as nib
from scipy.ndimage import gaussian_filter, zoom
from skimage.measure import regionprops
import SimpleITK as sitk
import voxelmorph as vxm
from voxelmorph.tf.layers import SpatialTransformer
import DeepDeformationMapRegistration.utils.constants as C
from DeepDeformationMapRegistration.utils.nifti_utils import save_nifti
from DeepDeformationMapRegistration.losses import StructuralSimilarity_simplified, NCC
from DeepDeformationMapRegistration.ms_ssim_tf import MultiScaleStructuralSimilarity
from DeepDeformationMapRegistration.utils.operators import min_max_norm
from DeepDeformationMapRegistration.utils.misc import resize_displacement_map
from DeepDeformationMapRegistration.utils.model_downloader import get_models_path
from importlib.util import find_spec
LOGGER = logging.getLogger(__name__)
MODELS_FILE = {'L': {'BL-N': './models/liver/bl_ncc.h5',
'BL-S': './models/liver/bl_ncc_ssim.h5',
'SG-ND': './models/liver/sg_ncc_dsc.h5',
'SD-NSD': './models/liver/sg_ncc_ssim_dsc.h5',
'UW-NSD': './models/liver/uw_ncc_ssim_dsc.h5',
'UW-NSDH': './models/liver/uw_ncc_ssim_dsc_hd.h5',
},
'B': {'BL-N': './models/brain/bl_ncc.h5',
'BL-S': './models/brain/bl_ncc_ssim.h5',
'SG-ND': './models/brain/sg_ncc_dsc.h5',
'SD-NSD': './models/brain/sg_ncc_ssim_dsc.h5',
'UW-NSD': './models/brain/uw_ncc_ssim_dsc.h5',
'UW-NSDH': './models/brain/uw_ncc_ssim_dsc_hd.h5',
}
}
IMAGE_INTPUT_SHAPE = np.asarray([128, 128, 128, 1])
def rigidly_align_images(image_1: str, image_2: str) -> nib.Nifti1Image:
"""
Rigidly align the images and resample to the same array size, to the dense displacement map is correct
"""
def resample_to_isotropic(image: sitk.Image) -> sitk.Image:
spacing = image.GetSpacing()
spacing = min(spacing)
resamp_spacing = [spacing] * image.GetDimension()
resamp_size = [int(round(or_size*or_space/spacing)) for or_size, or_space in zip(image.GetSize(), image.GetSpacing())]
return sitk.Resample(image,
resamp_size, sitk.Transform(), sitk.sitkLinear,image.GetOrigin(),
resamp_spacing, image.GetDirection(), 0, image.GetPixelID())
image_1 = sitk.ReadImage(image_1, sitk.sitkFloat32)
image_2 = sitk.ReadImage(image_2, sitk.sitkFloat32)
image_1 = resample_to_isotropic(image_1)
image_2 = resample_to_isotropic(image_2)
rig_reg = sitk.ImageRegistrationMethod()
rig_reg.SetMetricAsMeanSquares()
rig_reg.SetOptimizerAsRegularStepGradientDescent(4.0, 0.01, 200)
rig_reg.SetInitialTransform(sitk.TranslationTransform(image_1.GetDimension()))
rig_reg.SetInterpolator(sitk.sitkLinear)
print('Running rigid registration...')
rig_reg_trf = rig_reg.Execute(image_1, image_2)
print('Rigid registration completed\n----------------------------')
print('Optimizer stop condition: {}'.format(rig_reg.GetOptimizerStopConditionDescription()))
print('Iteration: {}'.format(rig_reg.GetOptimizerIteration()))
print('Metric value: {}'.format(rig_reg.GetMetricValue()))
resampler = sitk.ResampleImageFilter()
resampler.SetReferenceImage(image_1)
resampler.SetInterpolator(sitk.sitkLinear)
resampler.SetDefaultPixelValue(100)
resampler.SetTransform(rig_reg_trf)
image_2 = resampler.Execute(image_2)
# TODO: Build a common image to hold both image_1 and image_2
def pad_images(image_1: nib.Nifti1Image, image_2: nib.Nifti1Image):
"""
Align image_1 and image_2 by the top left corner and pad them to the largest dimensions along the three axes
"""
joint_image_shape = np.maximum(image_1.shape, image_2.shape)
pad_1 = [[0, p] for p in joint_image_shape - image_1.shape]
pad_2 = [[0, p] for p in joint_image_shape - image_2.shape]
image_1_padded = np.pad(image_1.dataobj, pad_1, mode='edge').astype(np.float32)
image_2_padded = np.pad(image_2.dataobj, pad_2, mode='edge').astype(np.float32)
return image_1_padded, image_2_padded
def pad_displacement_map(disp_map: np.ndarray, crop_min: np.ndarray, crop_max: np.ndarray, output_shape: (np.ndarray, list)) -> np.ndarray:
ret_val = disp_map
if np.all([d != i for d, i in zip(disp_map.shape[:3], output_shape)]):
padding = [[crop_min[i], max(0, output_shape[i] - crop_max[i])] for i in range(3)] + [[0, 0]]
ret_val = np.pad(disp_map, padding, mode='constant')
return ret_val
def run_livermask(input_image_path, outputdir, filename: str = 'segmentation') -> np.ndarray:
assert find_spec('livermask'), 'Livermask is not available'
LOGGER.info('Getting parenchyma segmentations...')
shutil.copy2(input_image_path, os.path.join(outputdir, f'{filename}.nii.gz'))
livermask_cmd = "{} -m livermask.livermask --input {} --output {}".format(sys.executable,
input_image_path,
os.path.join(outputdir,
f'{filename}.nii.gz'))
subprocess.run(livermask_cmd)
LOGGER.info('done!')
segmentation_path = os.path.join(outputdir, f'{filename}.nii.gz')
return np.asarray(nib.load(segmentation_path).dataobj, dtype=int)
def debug_save_image(image: (np.ndarray, nib.Nifti1Image), filename: str, outputdir: str, debug: bool = True):
def disp_map_modulus(disp_map, scale: float = None):
disp_map_mod = np.sqrt(np.sum(np.power(disp_map, 2), -1))
if scale:
min_disp = np.min(disp_map_mod)
max_disp = np.max(disp_map_mod)
disp_map_mod = disp_map_mod - min_disp / (max_disp - min_disp)
disp_map_mod *= scale
LOGGER.debug('Scaled displacement map to [0., 1.] range')
return disp_map_mod
if debug:
os.makedirs(os.path.join(outputdir, 'debug'), exist_ok=True)
if image.shape[-1] > 1:
image = disp_map_modulus(image, 1.)
save_nifti(image, os.path.join(outputdir, 'debug', filename+'.nii.gz'), verbose=False)
LOGGER.debug(f'Saved {filename} at {os.path.join(outputdir, filename + ".nii.gz")}')
def get_roi(image_filepath: str,
compute_segmentation: bool,
outputdir: str,
filename_filepath: str = 'segmentation',
segmentation_file: str = None,
debug: bool = False) -> list:
segm = None
if segmentation_file is None and compute_segmentation:
LOGGER.info(f'Computing segmentation using livermask. Only for liver in abdominal CTs')
try:
segm = run_livermask(image_filepath, outputdir, filename_filepath)
LOGGER.info(f'Loaded segmentation using livermask from {os.path.join(outputdir, filename_filepath)}')
except (AssertionError, FileNotFoundError) as er:
LOGGER.warning(er)
LOGGER.warning('No segmentation provided! Using the full volume')
pass
elif segmentation_file is not None:
segm = np.asarray(nib.load(segmentation_file).dataobj, dtype=int)
LOGGER.info(f'Loaded fixed segmentation from {segmentation_file}')
else:
LOGGER.warning('No segmentation provided! Using the full volume')
if segm is not None:
segm[segm > 0] = 1
ret_val = regionprops(segm)[0].bbox
debug_save_image(segm, f'img_1_{filename_filepath}', outputdir, debug)
else:
ret_val = [0, 0, 0] + list(nib.load(image_filepath).shape[:3])
LOGGER.debug(f'ROI found at coordinates {ret_val}')
return ret_val
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--fixed', type=str, help='Path to fixed image file (NIfTI)')
parser.add_argument('-m', '--moving', type=str, help='Path to moving segmentation image file (NIfTI)', default=None)
parser.add_argument('-F', '--fixedsegm', type=str, help='Path to fixed image segmentation file(NIfTI)',
default=None)
parser.add_argument('-M', '--movingsegm', type=str, help='Path to moving image file (NIfTI)')
parser.add_argument('-o', '--outputdir', type=str, help='Output directory', default='./Registration_output')
parser.add_argument('-a', '--anatomy', type=str, help='Anatomical structure: liver (L) (Default) or brain (B)',
default='L')
parser.add_argument('-s', '--make-segmentation', action='store_true', help='Try to create a segmentation for liver in CT images', default=False)
parser.add_argument('--gpu', type=int,
help='In case of multi-GPU systems, limits the execution to the defined GPU number',
default=None)
parser.add_argument('--model', type=str, help='Which model to use: BL-N, BL-S, SG-ND, SG-NSD, UW-NSD, UW-NSDH',
default='UW-NSD')
parser.add_argument('--debug', '-d', action='store_true', help='Produce additional debug information', default=False)
parser.add_argument('-c', '--clear-outputdir', action='store_true', help='Clear output folder if this has content', default=False)
args = parser.parse_args()
assert os.path.exists(args.fixed), 'Fixed image not found'
assert os.path.exists(args.moving), 'Moving image not found'
assert args.model in C.MODEL_TYPES.keys(), 'Invalid model type'
assert args.anatomy in C.ANATOMIES.keys(), 'Invalid anatomy option'
if os.path.exists(args.outputdir) and len(os.listdir(args.outputdir)):
if args.clear_outputdir:
erase = 'y'
else:
erase = input('Output directory is not empty, erase content? (y/n)')
if erase.lower() in ['y', 'yes']:
shutil.rmtree(args.outputdir, ignore_errors=True)
print('Erased directory: ' + args.outputdir)
elif erase.lower() in ['n', 'no']:
args.outputdir = os.path.join(args.outputdir, datetime.datetime.now().strftime('%H%M%S_%Y%m%d'))
print('New output directory: ' + args.outputdir)
os.makedirs(args.outputdir, exist_ok=True)
log_format = '%(asctime)s [%(levelname)s]:\t%(message)s'
logging.basicConfig(filename=os.path.join(args.outputdir, 'log.log'), filemode='w',
format=log_format, datefmt='%Y-%m-%d %H:%M:%S')
stdout_handler = logging.StreamHandler(sys.stdout)
stdout_handler.setFormatter(logging.Formatter(log_format, datefmt='%Y-%m-%d %H:%M:%S'))
LOGGER.addHandler(stdout_handler)
if isinstance(args.gpu, int):
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu) # Check availability before running using 'nvidia-smi'
LOGGER.setLevel('INFO')
if args.debug:
LOGGER.setLevel('DEBUG')
LOGGER.debug('DEBUG MODE ENABLED')
# Load the file and preprocess it
LOGGER.info('Loading image files')
fixed_image_or = nib.load(args.fixed)
moving_image_or = nib.load(args.moving)
image_shape_or = np.asarray(fixed_image_or.shape)
fixed_image_or, moving_image_or = pad_images(fixed_image_or, moving_image_or)
fixed_image_or = fixed_image_or[..., np.newaxis] # add channel dim
moving_image_or = moving_image_or[..., np.newaxis] # add channel dim
debug_save_image(fixed_image_or, 'img_0_loaded_fix_image', args.outputdir, args.debug)
debug_save_image(moving_image_or, 'img_0_loaded_moving_image', args.outputdir, args.debug)
# TF stuff
LOGGER.info('Setting up configuration')
config = tf.compat.v1.ConfigProto() # device_count={'GPU':0})
config.gpu_options.allow_growth = True
config.log_device_placement = False ## to log device placement (on which device the operation ran)
config.allow_soft_placement = True
sess = tf.compat.v1.Session(config=config)
tf.compat.v1.keras.backend.set_session(sess)
# Preprocess data
# 1. Run Livermask to get the mask around the liver in both the fixed and moving image
LOGGER.info('Getting ROI')
fixed_segm_bbox = get_roi(args.fixed, args.make_segmentation, args.outputdir,
'fixed_segmentation', args.fixedsegm, args.debug)
moving_segm_bbox = get_roi(args.moving, args.make_segmentation, args.outputdir,
'moving_segmentation', args.movingsegm, args.debug)
crop_min = np.amin(np.vstack([fixed_segm_bbox[:3], moving_segm_bbox[:3]]), axis=0)
crop_max = np.amax(np.vstack([fixed_segm_bbox[3:], moving_segm_bbox[3:]]), axis=0)
# 2.2 Crop the fixed and moving images using such boxes
fixed_image = fixed_image_or[crop_min[0]: crop_max[0],
crop_min[1]: crop_max[1],
crop_min[2]: crop_max[2], ...]
debug_save_image(fixed_image, 'img_2_cropped_fixed_image', args.outputdir, args.debug)
moving_image = moving_image_or[crop_min[0]: crop_max[0],
crop_min[1]: crop_max[1],
crop_min[2]: crop_max[2], ...]
debug_save_image(moving_image, 'img_2_cropped_moving_image', args.outputdir, args.debug)
image_shape_crop = fixed_image.shape
# 2.3 Resize the images to the expected input size
zoom_factors = IMAGE_INTPUT_SHAPE / image_shape_crop
fixed_image = zoom(fixed_image, zoom_factors)
moving_image = zoom(moving_image, zoom_factors)
fixed_image = min_max_norm(fixed_image)
moving_image = min_max_norm(moving_image)
debug_save_image(fixed_image, 'img_3_preproc_fixed_image', args.outputdir, args.debug)
debug_save_image(moving_image, 'img_3_preproc_moving_image', args.outputdir, args.debug)
# 3. Build the whole graph
LOGGER.info('Building TF graph')
### METRICS GRAPH ###
fix_img_ph = tf.compat.v1.placeholder(tf.float32, (1, None, None, None, 1), name='fix_img')
pred_img_ph = tf.compat.v1.placeholder(tf.float32, (1, None, None, None, 1), name='pred_img')
ssim_tf = StructuralSimilarity_simplified(patch_size=2, dim=3, dynamic_range=1.).metric(fix_img_ph, pred_img_ph)
ncc_tf = NCC(image_shape_or).metric(fix_img_ph, pred_img_ph)
mse_tf = vxm.losses.MSE().loss(fix_img_ph, pred_img_ph)
ms_ssim_tf = MultiScaleStructuralSimilarity(max_val=1., filter_size=3).metric(fix_img_ph, pred_img_ph)
LOGGER.info(f'Using model: {"Brain" if args.anatomy == "B" else "Liver"} -> {args.model}')
MODEL_FILE = get_models_path(args.anatomy, args.model, os.getcwd()) # MODELS_FILE[args.anatomy][args.model]
# try:
# network = tf.keras.models.load_model(MODEL_FILE,
# {'VxmDense': vxm.networks.VxmDense,
# # 'VxmDenseSemiSupervisedSeg': vxm.networks.VxmDenseSemiSupervisedSeg,
# 'AdamAccumulated': AdamAccumulated
# },
# compile=False)
# except ValueError as e:
# enc_features = [32, 64, 128, 256, 512, 1024] # const.ENCODER_FILTERS
# dec_features = enc_features[::-1] + [16, 16] # const.ENCODER_FILTERS[::-1]
# nb_features = [enc_features, dec_features]
# if re.search('^UW|SEGGUIDED_', MODEL_FILE):
# network = vxm.networks.VxmDense(inshape=IMAGE_INTPUT_SHAPE[:-1],
# nb_unet_features=nb_features,
# int_steps=0,
# int_downsize=1,
# seg_downsize=1)
# else:
# network = vxm.networks.VxmDense(inshape=IMAGE_INTPUT_SHAPE[:-1],
# nb_unet_features=nb_features,
# int_steps=0)
# network.load_weights(MODEL_FILE, by_name=True)
enc_features = [32, 64, 128, 256, 512, 1024] # const.ENCODER_FILTERS
dec_features = enc_features[::-1] + [16, 16] # const.ENCODER_FILTERS[::-1]
nb_features = [enc_features, dec_features]
network = vxm.networks.VxmDense(inshape=IMAGE_INTPUT_SHAPE[:-1],
nb_unet_features=nb_features,
int_steps=0)
network.load_weights(MODEL_FILE, by_name=True)
network.trainable = False
registration_model = network.get_registration_model()
deb_model = network.apply_transform
LOGGER.info('Computing registration')
with sess.as_default():
if args.debug:
registration_model.summary(line_length=C.SUMMARY_LINE_LENGTH)
LOGGER.info('Computing displacement map...')
time_disp_map_start = time.time()
# disp_map = registration_model.predict([moving_image[np.newaxis, ...], fixed_image[np.newaxis, ...]])
p, disp_map = network.predict([moving_image[np.newaxis, ...], fixed_image[np.newaxis, ...]])
time_disp_map_end = time.time()
LOGGER.info('\t... done')
debug_save_image(np.squeeze(disp_map), 'disp_map_0_raw', args.outputdir, args.debug)
debug_save_image(p[0, ...], 'img_4_net_pred_image', args.outputdir, args.debug)
# pred_image = min_max_norm(pred_image)
# pred_image_isot = zoom(pred_image[0, ...], zoom_factors, order=3)[np.newaxis, ...]
# fixed_image_isot = zoom(fixed_image[0, ...], zoom_factors, order=3)[np.newaxis, ...]
# Up sample the displacement map to the full res
LOGGER.info('Scaling displacement map...')
trf = np.eye(4)
np.fill_diagonal(trf, 1/zoom_factors)
disp_map = resize_displacement_map(np.squeeze(disp_map), None, trf)
debug_save_image(np.squeeze(disp_map), 'disp_map_1_upsampled', args.outputdir, args.debug)
disp_map_or = pad_displacement_map(disp_map, crop_min, crop_max, image_shape_or)
debug_save_image(np.squeeze(disp_map_or), 'disp_map_2_padded', args.outputdir, args.debug)
disp_map_or = gaussian_filter(disp_map_or, 5)
debug_save_image(np.squeeze(disp_map_or), 'disp_map_3_smoothed', args.outputdir, args.debug)
LOGGER.info('\t... done')
LOGGER.info('Applying displacement map...')
time_pred_img_start = time.time()
pred_image = SpatialTransformer(interp_method='linear', indexing='ij', single_transform=False)([moving_image_or[np.newaxis, ...], disp_map_or[np.newaxis, ...]]).eval()
time_pred_img_end = time.time()
LOGGER.info('\t... done')
LOGGER.info('Computing metrics...')
ssim, ncc, mse, ms_ssim = sess.run([ssim_tf, ncc_tf, mse_tf, ms_ssim_tf],
{'fix_img:0': fixed_image_or[np.newaxis, ...], 'pred_img:0': pred_image})
ssim = np.mean(ssim)
ms_ssim = ms_ssim[0]
pred_image = pred_image[0, ...]
save_nifti(pred_image, os.path.join(args.outputdir, 'pred_image.nii.gz'))
np.savez_compressed(os.path.join(args.outputdir, 'displacement_map.npz'), disp_map_or)
LOGGER.info('Predicted image (full image) and displacement map saved in: '.format(args.outputdir))
LOGGER.info(f'Displacement map prediction time: {time_disp_map_end - time_disp_map_start} s')
LOGGER.info(f'Predicted image time: {time_pred_img_end - time_pred_img_start} s')
LOGGER.info('Similarity metrics (Full image)\n------------------')
LOGGER.info('SSIM: {:.03f}'.format(ssim))
LOGGER.info('NCC: {:.03f}'.format(ncc))
LOGGER.info('MSE: {:.03f}'.format(mse))
LOGGER.info('MS SSIM: {:.03f}'.format(ms_ssim))
ssim, ncc, mse, ms_ssim = sess.run([ssim_tf, ncc_tf, mse_tf, ms_ssim_tf],
{'fix_img:0': fixed_image[np.newaxis, ...], 'pred_img:0': p})
ssim = np.mean(ssim)
ms_ssim = ms_ssim[0]
LOGGER.info('\nSimilarity metrics (ROI)\n------------------')
LOGGER.info('SSIM: {:.03f}'.format(ssim))
LOGGER.info('NCC: {:.03f}'.format(ncc))
LOGGER.info('MSE: {:.03f}'.format(mse))
LOGGER.info('MS SSIM: {:.03f}'.format(ms_ssim))
del registration_model
LOGGER.info('Done')
exit(0)
if __name__ == '__main__':
main()