def build_model_inputs(path_label_maps,
n_labels,
batchsize=1,
n_channels=1,
generation_classes=None,
prior_distributions='uniform',
prior_means=None,
prior_stds=None,
use_specific_stats_for_channel=False,
mix_prior_and_random=False,
apply_linear_trans=True,
scaling_bounds=None,
rotation_bounds=None,
shearing_bounds=None,
background_paths=None):
"""
This function builds a generator to be fed to the lab2im model. It enables to generate all the required inputs,
according to the operations performed in the model.
:param path_label_maps: list of the paths of the input label maps.
:param n_labels: number of labels in the input label maps.
:param batchsize: (optional) numbers of images to generate per mini-batch. Default is 1.
:param n_channels: (optional) number of channels to be synthetised. Default is 1.
:param generation_classes: (optional) Indices regrouping generation labels into classes of same intensity
distribution. Regouped labels will thus share the same Gaussian when samling a new image. Can be a sequence or a
1d numpy array. It should have the same length as generation_labels, and contain values between 0 and K-1, where K
is the total number of classes. Default is all labels have different classes.
:param prior_distributions: (optional) type of distribution from which we sample the GMM parameters.
Can either be 'uniform', or 'normal'. Default is 'uniform'.
:param prior_means: (optional) hyperparameters controlling the prior distributions of the GMM means. Because
these prior distributions are uniform or normal, they require by 2 hyperparameters. Thus prior_means can be:
1) a sequence of length 2, directly defining the two hyperparameters: [min, max] if prior_distributions is
uniform, [mean, std] if the distribution is normal. The GMM means of are independently sampled at each
mini_batch from the same distribution.
2) an array of shape (2, K), where K is the number of classes (K=len(generation_labels) if generation_classes is
not given). The mean of the Gaussian distribution associated to class k in [0, ...K-1] is sampled at each mini-batch
from U(prior_means[0,k], prior_means[1,k]) if prior_distributions is uniform, or from
N(prior_means[0,k], prior_means[1,k]) if prior_distributions is normal.
3) an array of shape (2*n_mod, K), where each block of two rows is associated to hyperparameters derived
from different modalities. In this case, if use_specific_stats_for_channel is False, we first randomly select a
modality from the n_mod possibilities, and we sample the GMM means like in 2).
If use_specific_stats_for_channel is True, each block of two rows correspond to a different channel
(n_mod=n_channels), thus we select the corresponding block to each channel rather than randomly drawing it.
4) the path to such a numpy array.
Default is None, which corresponds to prior_means = [25, 225].
:param prior_stds: (optional) same as prior_means but for the standard deviations of the GMM.
Default is None, which corresponds to prior_stds = [5, 25].
:param use_specific_stats_for_channel: (optional) whether the i-th block of two rows in the prior arrays must be
only used to generate the i-th channel. If True, n_mod should be equal to n_channels. Default is False.
:param mix_prior_and_random: (optional) if prior_means is not None, enables to reset the priors to their default
values for half of thes cases, and thus generate images of random contrast.
:param apply_linear_trans: (optional) whether to apply affine deformation. Default is True.
:param scaling_bounds: (optional) if apply_linear_trans is True, the scaling factor for each dimension is
sampled from a uniform distribution of predefined bounds. Can either be:
1) a number, in which case the scaling factor is independently sampled from the uniform distribution of bounds
(1-scaling_bounds, 1+scaling_bounds) for each dimension.
2) a sequence, in which case the scaling factor is sampled from the uniform distribution of bounds
(1-scaling_bounds[i], 1+scaling_bounds[i]) for the i-th dimension.
3) a numpy array of shape (2, n_dims), in which case the scaling factor is sampled from the uniform distribution
of bounds (scaling_bounds[0, i], scaling_bounds[1, i]) for the i-th dimension.
If None (default), scaling_range = 0.15
:param rotation_bounds: (optional) same as scaling bounds but for the rotation angle, except that for cases 1
and 2, the bounds are centred on 0 rather than 1, i.e. (0+rotation_bounds[i], 0-rotation_bounds[i]).
If None (default), rotation_bounds = 15.
:param shearing_bounds: (optional) same as scaling bounds. If None (default), shearing_bounds = 0.01.
:param background_paths: (optional) list of paths of label maps to replace the soft brain tissues (label 258) with.
"""
# get label info
_, _, n_dims, _, _, _ = utils.get_volume_info(path_label_maps[0])
# allocate unique class to each label if generation classes is not given
if generation_classes is None:
generation_classes = np.arange(n_labels)
# Generate!
while True:
# randomly pick as many images as batchsize
indices = npr.randint(len(path_label_maps), size=batchsize)
# initialise input lists
list_label_maps = []
list_means = []
list_stds = []
list_affine_transforms = []
for idx in indices:
# add labels to inputs
y = utils.load_volume(path_label_maps[idx], dtype='int', aff_ref=np.eye(4))
if background_paths is not None:
idx_258 = np.where(y == 258)
if np.any(idx_258):
background = utils.load_volume(background_paths[npr.randint(len(background_paths))],
dtype='int', aff_ref=np.eye(4))
background_shape = background.shape
if np.all(np.array(background_shape) == background_shape[0]): # flip if same dimensions
background = np.flip(background, tuple([i for i in range(3) if np.random.normal() > 0]))
assert background.shape == y.shape, 'background patches should have same shape than training ' \
'labels. Had {0} and {1}'.format(background.shape, y.shape)
y[idx_258] = background[idx_258]
list_label_maps.append(utils.add_axis(y, axis=-2))
# add means and standard deviations to inputs
means = np.empty((n_labels, 0))
stds = np.empty((n_labels, 0))
for channel in range(n_channels):
# retrieve channel specific stats if necessary
if isinstance(prior_means, np.ndarray):
if (prior_means.shape[0] > 2) & use_specific_stats_for_channel:
if prior_means.shape[0] / 2 != n_channels:
raise ValueError("the number of blocks in prior_means does not match n_channels. This "
"message is printed because use_specific_stats_for_channel is True.")
tmp_prior_means = prior_means[2 * channel:2 * channel + 2, :]
else:
tmp_prior_means = prior_means
else:
tmp_prior_means = prior_means
if (prior_means is not None) & mix_prior_and_random & (npr.uniform() > 0.5):
tmp_prior_means = None
if isinstance(prior_stds, np.ndarray):
if (prior_stds.shape[0] > 2) & use_specific_stats_for_channel:
if prior_stds.shape[0] / 2 != n_channels:
raise ValueError("the number of blocks in prior_stds does not match n_channels. This "
"message is printed because use_specific_stats_for_channel is True.")
tmp_prior_stds = prior_stds[2 * channel:2 * channel + 2, :]
else:
tmp_prior_stds = prior_stds
else:
tmp_prior_stds = prior_stds
if (prior_stds is not None) & mix_prior_and_random & (npr.uniform() > 0.5):
tmp_prior_stds = None
# draw means and std devs from priors
tmp_classes_means = utils.draw_value_from_distribution(tmp_prior_means, n_labels, prior_distributions,
125., 100., positive_only=True)
tmp_classes_stds = utils.draw_value_from_distribution(tmp_prior_stds, n_labels, prior_distributions,
15., 10., positive_only=True)
tmp_means = utils.add_axis(tmp_classes_means[generation_classes], -1)
tmp_stds = utils.add_axis(tmp_classes_stds[generation_classes], -1)
means = np.concatenate([means, tmp_means], axis=1)
stds = np.concatenate([stds, tmp_stds], axis=1)
list_means.append(utils.add_axis(means))
list_stds.append(utils.add_axis(stds))
# add linear transform to inputs
if apply_linear_trans:
# get affine transformation: rotate, scale, shear (translation done during random cropping)
scaling = utils.draw_value_from_distribution(scaling_bounds, size=n_dims, centre=1, default_range=.15)
if n_dims == 2:
rotation = utils.draw_value_from_distribution(rotation_bounds, default_range=15.0)
else:
rotation = utils.draw_value_from_distribution(rotation_bounds, size=n_dims, default_range=15.0)
shearing = utils.draw_value_from_distribution(shearing_bounds, size=n_dims**2-n_dims, default_range=.01)
affine_transform = utils.create_affine_transformation_matrix(n_dims, scaling, rotation, shearing)
list_affine_transforms.append(utils.add_axis(affine_transform))
# build list of inputs of augmentation model
list_inputs = [list_label_maps, list_means, list_stds]
if apply_linear_trans:
list_inputs.append(list_affine_transforms)
# concatenate individual input types if batchsize > 1
if batchsize > 1:
list_inputs = [np.concatenate(item, 0) for item in list_inputs]
else:
list_inputs = [item[0] for item in list_inputs]
yield list_inputs
def build_model_input_generator(images_paths,
labels_paths,
n_channels,
im_shape,
scaling_range=None,
rotation_range=None,
shearing_range=None,
nonlin_shape_fact=0.0625,
nonlin_std_dev=3,
batch_size=1):
# Generate!
while True:
# randomly pick as many images as batch_size
indices = npr.randint(len(images_paths), size=batch_size)
# initialise input tensors
images_all = []
labels_all = []
aff_all = []
nonlinear_field_all = []
for idx in indices:
# add image
image = utils.load_volume(images_paths[idx])
if n_channels > 1:
images_all.append(utils.add_axis(image, axis=0))
else:
images_all.append(utils.add_axis(image, axis=-2))
# add labels
labels = utils.load_volume(labels_paths[idx], dtype='int')
labels_all.append(utils.add_axis(labels, axis=-2))
# get affine transformation: rotate, scale, shear (translation done during random cropping)
n_dims, _ = utils.get_dims(im_shape)
scaling = utils.draw_value_from_distribution(scaling_range, size=n_dims, centre=1, default_range=.15)
if n_dims == 2:
rotation_angle = utils.draw_value_from_distribution(rotation_range, default_range=15.0)
else:
rotation_angle = utils.draw_value_from_distribution(rotation_range, size=n_dims, default_range=15.0)
shearing = utils.draw_value_from_distribution(shearing_range, size=n_dims ** 2 - n_dims, default_range=.01)
aff = utils.create_affine_transformation_matrix(n_dims, scaling, rotation_angle, shearing)
aff_all.append(utils.add_axis(aff))
# add non linear field
deform_shape = utils.get_resample_shape(im_shape, nonlin_shape_fact, len(im_shape))
nonlinear_field = npr.normal(loc=0, scale=nonlin_std_dev * npr.rand(), size=deform_shape)
nonlinear_field_all.append(utils.add_axis(nonlinear_field))
# build list of inputs of the augmentation model
inputs_vals = [images_all, labels_all, aff_all, nonlinear_field_all]
# put images and labels (concatenated if batch_size>1) into a tuple of 2 elements: (cat_images, cat_labels)
if batch_size > 1:
inputs_vals = [np.concatenate(item, 0) for item in inputs_vals]
else:
inputs_vals = [item[0] for item in inputs_vals]
yield inputs_vals
def means_stds_fs_labels_with_relations(means_range, std_devs_range, min_diff=15, head=True):
# draw gm wm and csf means
gm_wm_csf_means = np.zeros(3)
while (abs(gm_wm_csf_means[1] - gm_wm_csf_means[0]) < min_diff) | \
(abs(gm_wm_csf_means[1] - gm_wm_csf_means[2]) < min_diff) | \
(abs(gm_wm_csf_means[0] - gm_wm_csf_means[2]) < min_diff):
gm_wm_csf_means = utils.draw_value_from_distribution(means_range, 3, 'uniform', 125., 100., positive_only=True)
gm_wm_csf_means = utils.add_axis(gm_wm_csf_means, -1)
# apply relations
wm = gm_wm_csf_means[0]
gm = gm_wm_csf_means[1]
csf = gm_wm_csf_means[2]
csf_like = csf * npr.uniform(low=0.95, high=1.05)
alpha_thalamus = npr.uniform(low=0.4, high=0.9)
thalamus = alpha_thalamus*gm + (1-alpha_thalamus)*wm
cerebellum_wm = wm * npr.uniform(low=0.7, high=1.3)
cerebellum_gm = gm * npr.uniform(low=0.7, high=1.3)
caudate = gm * npr.uniform(low=0.9, high=1.1)
putamen = gm * npr.uniform(low=0.9, high=1.1)
hippocampus = gm * npr.uniform(low=0.9, high=1.1)
amygdala = gm * npr.uniform(low=0.9, high=1.1)
accumbens = caudate * npr.uniform(low=0.9, high=1.1)
pallidum = wm * npr.uniform(low=0.8, high=1.2)
brainstem = wm * npr.uniform(low=0.8, high=1.2)
alpha_ventralDC = npr.uniform(low=0.1, high=0.6)
ventralDC = alpha_ventralDC*gm + (1-alpha_ventralDC)*wm
alpha_choroid = npr.uniform(low=0.0, high=1.0)
choroid = alpha_choroid*csf + (1-alpha_choroid)*wm
# regroup structures
neutral_means = [np.zeros(1), csf_like, csf_like, brainstem, csf]
sided_means = [wm, gm, csf_like, csf_like, cerebellum_wm, cerebellum_gm, thalamus, caudate, putamen, pallidum,
hippocampus, amygdala, accumbens, ventralDC, choroid]
# draw std deviations
std = utils.draw_value_from_distribution(std_devs_range, 17, 'uniform', 15., 10., positive_only=True)
std = utils.add_axis(std, -1)
neutral_stds = [np.zeros(1), std[1], std[1], std[2], std[3]]
sided_stds = [std[4], std[5], std[1], std[1], std[6], std[7], std[8], std[9], std[10], std[11], std[12], std[13],
std[14], std[15], std[16]]
# add means and variances for extra head labels if necessary
if head:
# means
extra_means = utils.draw_value_from_distribution(means_range, 2, 'uniform', 125., 100., positive_only=True)
extra_means = utils.add_axis(extra_means, -1)
skull = extra_means[0]
soft_non_brain = extra_means[1]
eye = csf * npr.uniform(low=0.95, high=1.05)
optic_chiasm = wm * npr.uniform(low=0.8, high=1.2)
vessel = csf * npr.uniform(low=0.7, high=1.3)
neutral_means += [csf_like, optic_chiasm, skull, soft_non_brain, eye]
sided_means.insert(-1, vessel)
# std dev
extra_std = utils.draw_value_from_distribution(std_devs_range, 4, 'uniform', 15., 10., positive_only=True)
extra_std = utils.add_axis(extra_std, -1)
neutral_stds += [std[1], extra_std[0], extra_std[1], extra_std[2], std[1]]
sided_stds.insert(-1, extra_std[3])
means = np.concatenate([np.array(neutral_means), np.array(sided_means), np.array(sided_means)])
stds = np.concatenate([np.array(neutral_stds), np.array(sided_stds), np.array(sided_stds)])
return means, stds
def build_model_inputs(path_label_maps,
n_labels,
batchsize=1,
n_channels=1,
generation_classes=None,
prior_distributions='uniform',
prior_means=None,
prior_stds=None,
use_specific_stats_for_channel=False,
mix_prior_and_random=False):
"""
This function builds a generator to be fed to the lab2im model. It enables to generate all the required inputs,
according to the operations performed in the model.
:param path_label_maps: list of the paths of the input label maps.
:param n_labels: number of labels in the input label maps.
:param batchsize: (optional) numbers of images to generate per mini-batch. Default is 1.
:param n_channels: (optional) number of channels to be synthetised. Default is 1.
:param generation_classes: (optional) Indices regrouping generation labels into classes of same intensity
distribution. Regouped labels will thus share the same Gaussian when samling a new image. Can be a sequence or a
1d numpy array. It should have the same length as generation_labels, and contain values between 0 and K-1, where K
is the total number of classes. Default is all labels have different classes.
:param prior_distributions: (optional) type of distribution from which we sample the GMM parameters.
Can either be 'uniform', or 'normal'. Default is 'uniform'.
:param prior_means: (optional) hyperparameters controlling the prior distributions of the GMM means. Because
these prior distributions are uniform or normal, they require by 2 hyperparameters. Thus prior_means can be:
1) a sequence of length 2, directly defining the two hyperparameters: [min, max] if prior_distributions is
uniform, [mean, std] if the distribution is normal. The GMM means of are independently sampled at each
mini_batch from the same distribution.
2) an array of shape (2, K), where K is the number of classes (K=len(generation_labels) if generation_classes is
not given). The mean of the Gaussian distribution associated to class k in [0, ...K-1] is sampled at each mini-batch
from U(prior_means[0,k], prior_means[1,k]) if prior_distributions is uniform, or from
N(prior_means[0,k], prior_means[1,k]) if prior_distributions is normal.
3) an array of shape (2*n_mod, K), where each block of two rows is associated to hyperparameters derived
from different modalities. In this case, if use_specific_stats_for_channel is False, we first randomly select a
modality from the n_mod possibilities, and we sample the GMM means like in 2).
If use_specific_stats_for_channel is True, each block of two rows correspond to a different channel
(n_mod=n_channels), thus we select the corresponding block to each channel rather than randomly drawing it.
4) the path to such a numpy array.
Default is None, which corresponds to prior_means = [25, 225].
:param prior_stds: (optional) same as prior_means but for the standard deviations of the GMM.
Default is None, which corresponds to prior_stds = [5, 25].
:param use_specific_stats_for_channel: (optional) whether the i-th block of two rows in the prior arrays must be
only used to generate the i-th channel. If True, n_mod should be equal to n_channels. Default is False.
:param mix_prior_and_random: (optional) if prior_means is not None, enables to reset the priors to their default
values for half of thes cases, and thus generate images of random contrast.
"""
# get label info
_, _, n_dims, _, _, _ = utils.get_volume_info(path_label_maps[0])
# allocate unique class to each label if generation classes is not given
if generation_classes is None:
generation_classes = np.arange(n_labels)
# Generate!
while True:
# randomly pick as many images as batchsize
indices = npr.randint(len(path_label_maps), size=batchsize)
# initialise input lists
list_label_maps = []
list_means = []
list_stds = []
for idx in indices:
# add labels to inputs
lab = utils.load_volume(path_label_maps[idx], dtype='int', aff_ref=np.eye(4))
list_label_maps.append(utils.add_axis(lab, axis=[0, -1]))
# add means and standard deviations to inputs
means = np.empty((1, n_labels, 0))
stds = np.empty((1, n_labels, 0))
for channel in range(n_channels):
# retrieve channel specific stats if necessary
if isinstance(prior_means, np.ndarray):
if (prior_means.shape[0] > 2) & use_specific_stats_for_channel:
if prior_means.shape[0] / 2 != n_channels:
raise ValueError("the number of blocks in prior_means does not match n_channels. This "
"message is printed because use_specific_stats_for_channel is True.")
tmp_prior_means = prior_means[2 * channel:2 * channel + 2, :]
else:
tmp_prior_means = prior_means
else:
tmp_prior_means = prior_means
if (prior_means is not None) & mix_prior_and_random & (npr.uniform() > 0.5):
tmp_prior_means = None
if isinstance(prior_stds, np.ndarray):
if (prior_stds.shape[0] > 2) & use_specific_stats_for_channel:
if prior_stds.shape[0] / 2 != n_channels:
raise ValueError("the number of blocks in prior_stds does not match n_channels. This "
"message is printed because use_specific_stats_for_channel is True.")
tmp_prior_stds = prior_stds[2 * channel:2 * channel + 2, :]
else:
tmp_prior_stds = prior_stds
else:
tmp_prior_stds = prior_stds
if (prior_stds is not None) & mix_prior_and_random & (npr.uniform() > 0.5):
tmp_prior_stds = None
# draw means and std devs from priors
tmp_classes_means = utils.draw_value_from_distribution(tmp_prior_means, n_labels, prior_distributions,
125., 100., positive_only=True)
tmp_classes_stds = utils.draw_value_from_distribution(tmp_prior_stds, n_labels, prior_distributions,
15., 10., positive_only=True)
if npr.uniform() > 0.95: # reset the background to 0 in 10% of cases
tmp_classes_means[0] = 0
tmp_classes_stds[0] = 0
tmp_means = utils.add_axis(tmp_classes_means[generation_classes], axis=[0, -1])
tmp_stds = utils.add_axis(tmp_classes_stds[generation_classes], axis=[0, -1])
means = np.concatenate([means, tmp_means], axis=-1)
stds = np.concatenate([stds, tmp_stds], axis=-1)
list_means.append(means)
list_stds.append(stds)
# build list of inputs for generation model
list_inputs = [list_label_maps, list_means, list_stds]
if batchsize > 1: # concatenate each input type if batchsize > 1
list_inputs = [np.concatenate(item, 0) for item in list_inputs]
else:
list_inputs = [item[0] for item in list_inputs]
yield list_inputs
