def __init__(self,
labels_dir,
generation_labels=None,
output_labels=None,
n_neutral_labels=None,
batchsize=1,
n_channels=1,
target_res=None,
output_shape=None,
output_div_by_n=None,
prior_distributions='uniform',
generation_classes=None,
prior_means=None,
prior_stds=None,
use_specific_stats_for_channel=False,
mix_prior_and_random=False,
flipping=True,
scaling_bounds=0.15,
rotation_bounds=15,
shearing_bounds=.012,
translation_bounds=False,
nonlin_std=4.,
nonlin_shape_factor=0.0625,
randomise_res=False,
buil_distance_maps=False,
data_res=None,
thickness=None,
downsample=False,
blur_range=1.15,
bias_field_std=0.5,
bias_shape_factor=0.025):
"""
This class is wrapper around the labels_to_image_model model. It contains the GPU model that generates images
from labels maps, and a python generator that suplies the input data for this model.
To generate pairs of image/labels you can just call the method generate_image() on an object of this class.
:param labels_dir: path of folder with all input label maps, or to a single label map.
# IMPORTANT !!!
# Each time we provide a parameter with separate values for each axis (e.g. with a numpy array or a sequence),
# these values refer to the RAS axes.
# label maps-related parameters
:param generation_labels: (optional) list of all possible label values in the input label maps.
Default is None, where the label values are directly gotten from the provided label maps.
If not None, can be a sequence or a 1d numpy array, or the path to a 1d numpy array.
If flipping is true (i.e. right/left flipping is enabled), generation_labels should be organised as follows:
background label first, then non-sided labels (e.g. CSF, brainstem, etc.), then all the structures of the same
hemisphere (can be left or right), and finally all the corresponding contralateral structures in the same order.
:param output_labels: (optional) list of all the label values to keep in the output label maps (in no particular
order). Should be a subset of the values contained in generation_labels.
Label values that are in generation_labels but not in output_labels are reset to zero.
Can be a sequence, a 1d numpy array, or the path to a 1d numpy array.
By default output labels are equal to generation labels.
:param n_neutral_labels: (optional) number of non-sided generation labels.
Default is total number of label values.
# output-related parameters
: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 target_res: (optional) target resolution of the generated images and corresponding label maps.
If None, the outputs will have the same resolution as the input label maps.
Can be a number (isotropic resolution), a sequence, a 1d numpy array, or the path to a 1d numpy array.
:param output_shape: (optional) shape of the output image, obtained by randomly cropping the generated image.
Can be an integer (same size in all dimensions), a sequence, a 1d numpy array, or the path to a 1d numpy array.
Default is None, where no cropping is performed.
:param output_div_by_n: (optional) forces the output shape to be divisible by this value. It overwrites
output_shape if necessary. Can be an integer (same size in all dimensions), a sequence, a 1d numpy array, or
the path to a 1d numpy array.
# GMM-sampling parameters
: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, a
1d numpy array, or the path to 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 (K=len(generation_labels)).
: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, and 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.
# spatial deformation parameters
:param flipping: (optional) whether to introduce right/left random flipping. Default is True.
:param scaling_bounds: (optional) range of the random saling to apply at each mini-batch. 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.
4) False, in which case scaling is completely turned off.
Default is scaling_bounds = 0.15 (case 1)
: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]].
Default is rotation_bounds = 15.
:param shearing_bounds: (optional) same as scaling bounds. Default is shearing_bounds = 0.012.
:param translation_bounds: (optional) same as scaling bounds. Default is translation_bounds = False, but we
encourage using it when cropping is deactivated (i.e. when output_shape=None in BrainGenerator).
:param nonlin_std: (optional) Maximum value for the standard deviation of the normal distribution from which we
sample the first tensor for synthesising the deformation field. Set to 0 if you wish to completely turn the
elastic deformation off.
:param nonlin_shape_factor: (optional) if nonlin_std is strictly positive, factor between the shapes of the
input label maps and the shape of the input non-linear tensor.
# blurring/resampling parameters
:param randomise_res: (optional) whether to mimic images that would have been 1) acquired at low resolution, and
2) resampled to high esolution. The low resolution is uniformly resampled at each minibatch from [1mm, 9mm].
In that process, the images generated by sampling the GMM are:
1) blurred at the sampled LR, 2) downsampled at LR, and 3) resampled at target_resolution.
:param data_res: (optional) specific acquisition resolution to mimic, as opposed to random resolution sampled
when randomis_res is True. This triggers a blurring which mimics the acquisition resolution, but downsampling
is optional (see param downsample). Default for data_res is None, where images are slighlty blurred.
If the generated images are uni-modal, data_res can be a number (isotropic acquisition resolution), a sequence,
a 1d numpy array, or the path to a 1d numy array. In the multi-modal case, it should be given as a umpy array (
or a path) of size (n_mod, n_dims), where each row is the acquisition resolution of the corresponding channel.
:param thickness: (optional) if data_res is provided, we can further specify the slice thickness of the low
resolution images to mimic. Must be provided in the same format as data_res. Default thickness = data_res.
:param downsample: (optional) whether to actually downsample the volume images to data_res after blurring.
Default is False, except when thickness is provided, and thickness < data_res.
:param blur_range: (optional) Randomise the standard deviation of the blurring kernels, (whether data_res is
given or not). At each mini_batch, the standard deviation of the blurring kernels are multiplied by a
coefficient sampled from a uniform distribution with bounds [1/blur_range, blur_range].
If None, no randomisation. Default is 1.15.
# bias field parameters
:param bias_field_std: (optional) If strictly positive, this triggers the corruption of synthesised images with
a bias field. It is obtained by sampling a first small tensor from a normal distribution, resizing it to full
size, and rescaling it to positive values by taking the voxel-wise exponential. bias_field_std designates the
std dev of the normal distribution from which we sample the first tensor. Set to 0 to deactivate bias field.
:param bias_shape_factor: (optional) If bias_field_std is strictly positive, this designates the ratio between
the size of the input label maps and the size of the first sampled tensor for synthesising the bias field.
"""
# prepare data files
self.labels_paths = utils.list_images_in_folder(labels_dir)
# generation parameters
self.labels_shape, self.aff, self.n_dims, _, self.header, self.atlas_res = \
utils.get_volume_info(self.labels_paths[0], aff_ref=np.eye(4))
self.n_channels = n_channels
if generation_labels is not None:
self.generation_labels = utils.load_array_if_path(generation_labels)
else:
self.generation_labels, _ = utils.get_list_labels(labels_dir=labels_dir)
if output_labels is not None:
self.output_labels = utils.load_array_if_path(output_labels)
else:
self.output_labels = self.generation_labels
if n_neutral_labels is not None:
self.n_neutral_labels = n_neutral_labels
else:
self.n_neutral_labels = self.generation_labels.shape[0]
self.target_res = utils.load_array_if_path(target_res)
self.batchsize = batchsize
# preliminary operations
self.flipping = flipping
self.output_shape = utils.load_array_if_path(output_shape)
self.output_div_by_n = output_div_by_n
# GMM parameters
self.prior_distributions = prior_distributions
if generation_classes is not None:
self.generation_classes = utils.load_array_if_path(generation_classes)
assert self.generation_classes.shape == self.generation_labels.shape, \
'if provided, generation labels should have the same shape as generation_labels'
unique_classes = np.unique(self.generation_classes)
assert np.array_equal(unique_classes, np.arange(np.max(unique_classes)+1)), \
'generation_classes should a linear range between 0 and its maximum value.'
else:
self.generation_classes = np.arange(self.generation_labels.shape[0])
self.prior_means = utils.load_array_if_path(prior_means)
self.prior_stds = utils.load_array_if_path(prior_stds)
self.use_specific_stats_for_channel = use_specific_stats_for_channel
# linear transformation parameters
self.scaling_bounds = utils.load_array_if_path(scaling_bounds)
self.rotation_bounds = utils.load_array_if_path(rotation_bounds)
self.shearing_bounds = utils.load_array_if_path(shearing_bounds)
self.translation_bounds = utils.load_array_if_path(translation_bounds)
# elastic transformation parameters
self.nonlin_std = nonlin_std
self.nonlin_shape_factor = nonlin_shape_factor
# blurring parameters
self.randomise_res = randomise_res
self.buil_distance_maps = buil_distance_maps
self.data_res = utils.load_array_if_path(data_res)
assert not (self.randomise_res & (self.data_res is not None)), \
'randomise_res and data_res cannot be provided at the same time'
self.thickness = utils.load_array_if_path(thickness)
self.downsample = downsample
self.blur_range = blur_range
# bias field parameters
self.bias_field_std = bias_field_std
self.bias_shape_factor = bias_shape_factor
# build transformation model
self.labels_to_image_model, self.model_output_shape = self._build_labels_to_image_model()
# build generator for model inputs
self.model_inputs_generator = self._build_model_inputs_generator(mix_prior_and_random)
# build brain generator
self.brain_generator = self._build_brain_generator()
def predict(path_images,
path_segmentations,
path_model,
segmentation_labels,
n_neutral_labels=None,
path_posteriors=None,
path_resampled=None,
path_volumes=None,
segmentation_label_names=None,
padding=None,
cropping=None,
target_res=1.,
gradients=False,
flip=True,
topology_classes=None,
sigma_smoothing=0.5,
keep_biggest_component=True,
conv_size=3,
n_levels=5,
nb_conv_per_level=2,
unet_feat_count=24,
feat_multiplier=2,
activation='elu',
gt_folder=None,
evaluation_labels=None,
mask_folder=None,
list_incorrect_labels=None,
list_correct_labels=None,
compute_distances=False,
recompute=True,
verbose=True):
"""
This function uses trained models to segment images.
It is crucial that the inputs match the architecture parameters of the trained model.
:param path_images: path of the images to segment. Can be the path to a directory or the path to a single image.
:param path_segmentations: path where segmentations will be writen.
Should be a dir, if path_images is a dir, and a file if path_images is a file.
:param path_model: path ot the trained model.
:param segmentation_labels: List of labels for which to compute Dice scores. It should be the same list as the
segmentation_labels used in training.
:param n_neutral_labels: (optional) if the label maps contain some right/left specific labels and if test-time
flipping is applied (see parameter 'flip'), please provide the number of non-sided labels (including background).
It should be the same value as for training. Default is None.
:param path_posteriors: (optional) path where posteriors will be writen.
Should be a dir, if path_images is a dir, and a file if path_images is a file.
:param path_resampled: (optional) path where images resampled to 1mm isotropic will be writen.
We emphasise that images are resampled as soon as the resolution in one of the axes is not in the range [0.9; 1.1].
Should be a dir, if path_images is a dir, and a file if path_images is a file. Default is None, where resampled
images are not saved.
:param path_volumes: (optional) path of a csv file where the soft volumes of all segmented regions will be writen.
The rows of the csv file correspond to subjects, and the columns correspond to segmentation labels.
The soft volume of a structure corresponds to the sum of its predicted probability map.
:param segmentation_label_names: (optional) List of names correponding to the names of the segmentation labels.
Only used when path_volumes is provided. Must be of the same size as segmentation_labels. Can be given as a
list, a numpy array of strings, or the path to such a numpy array. Default is None.
:param padding: (optional) pad the images to the specified shape before predicting the segmentation maps.
Can be an int, a sequence or a 1d numpy array.
:param cropping: (optional) crop the images to the specified shape before predicting the segmentation maps.
If padding and cropping are specified, images are padded before being cropped.
Can be an int, a sequence or a 1d numpy array.
:param target_res: (optional) target resolution at which the network operates (and thus resolution of the output
segmentations). This must match the resolution of the training data ! target_res is used to automatically resampled
the images with resolutions outside [target_res-0.05, target_res+0.05].
Can be a sequence, a 1d numpy array. Set to None to disable the automatic resampling. Default is 1mm.
:param flip: (optional) whether to perform test-time augmentation, where the input image is segmented along with
a right/left flipped version on it. If set to True (default), be careful because this requires more memory.
:param topology_classes: List of classes corresponding to all segmentation labels, in order to group them into
classes, for each of which we will operate a smooth version of biggest connected component.
Can be a sequence, a 1d numpy array, or the path to a numpy 1d array in the same order as segmentation_labels.
Default is None, where no topological analysis is performed.
:param sigma_smoothing: (optional) If not None, the posteriors are smoothed with a gaussian kernel of the specified
standard deviation.
:param keep_biggest_component: (optional) whether to only keep the biggest component in the predicted segmentation.
This is applied independently of topology_classes, and it is applied to the whole segmentation
:param conv_size: (optional) size of unet's convolution masks. Default is 3.
:param n_levels: (optional) number of levels for unet. Default is 5.
:param nb_conv_per_level: (optional) number of convolution layers per level. Default is 2.
:param unet_feat_count: (optional) number of features for the first layer of the unet. Default is 24.
:param feat_multiplier: (optional) multiplicative factor for the number of feature for each new level. Default is 2.
:param activation: (optional) activation function. Can be 'elu', 'relu'.
:param gt_folder: (optional) path of the ground truth label maps corresponding to the input images. Should be a dir,
if path_images is a dir, or a file if path_images is a file.
Providing a gt_folder will trigger a Dice evaluation, where scores will be writen along with the path_segmentations.
Specifically, the scores are contained in a numpy array, where labels are in rows, and subjects in columns.
:param evaluation_labels: (optional) if gt_folder is True you can evaluate the Dice scores on a subset of the
segmentation labels, by providing another label list here. Can be a sequence, a 1d numpy array, or the path to a
numpy 1d array. Default is np.unique(segmentation_labels).
:param mask_folder: (optional) path of masks that will be used to mask out some parts of the obtained segmentations
during the evaluation. Default is None, where nothing is masked.
:param list_incorrect_labels: (optional) this option enables to replace some label values in the obtained
segmentations by other label values. Can be a list, a 1d numpy array, or the path to such an array.
:param list_correct_labels: (optional) list of values to correct the labels specified in list_incorrect_labels.
Correct values must have the same order as their corresponding value in list_incorrect_labels.
:param compute_distances: (optional) whether to add Hausdorff and mean surface distance evaluations to the default
Dice evaluation. Default is True.
:param recompute: (optional) whether to recompute segmentations that were already computed. This also applies to
Dice scores, if gt_folder is not None. Default is True.
:param verbose: (optional) whether to print out info about the remaining number of cases.
"""
# prepare input/output filepaths
path_images, path_segmentations, path_posteriors, path_resampled, path_volumes, compute = \
prepare_output_files(path_images, path_segmentations, path_posteriors, path_resampled, path_volumes, recompute)
# get label list
segmentation_labels, _ = utils.get_list_labels(
label_list=segmentation_labels)
n_labels = len(segmentation_labels)
# get unique label values, and build correspondance table between contralateral structures if necessary
if (n_neutral_labels is not None) & flip:
n_sided_labels = int((n_labels - n_neutral_labels) / 2)
lr_corresp = np.stack([
segmentation_labels[n_neutral_labels:n_neutral_labels +
n_sided_labels],
segmentation_labels[n_neutral_labels + n_sided_labels:]
])
segmentation_labels, indices = np.unique(segmentation_labels,
return_index=True)
lr_corresp_unique, lr_corresp_indices = np.unique(lr_corresp[0, :],
return_index=True)
lr_corresp_unique = np.stack(
[lr_corresp_unique, lr_corresp[1, lr_corresp_indices]])
lr_corresp_unique = lr_corresp_unique[:, 1:] if not np.all(
lr_corresp_unique[:, 0]) else lr_corresp_unique
lr_indices = np.zeros_like(lr_corresp_unique)
for i in range(lr_corresp_unique.shape[0]):
for j, lab in enumerate(lr_corresp_unique[i]):
lr_indices[i, j] = np.where(segmentation_labels == lab)[0]
else:
segmentation_labels, indices = np.unique(segmentation_labels,
return_index=True)
lr_indices = None
# prepare topology classes
if topology_classes is not None:
topology_classes = utils.load_array_if_path(
topology_classes, load_as_numpy=True)[indices]
# prepare volume file if needed
if path_volumes is not None:
if segmentation_label_names is not None:
segmentation_label_names = utils.load_array_if_path(
segmentation_label_names)[indices]
csv_header = [[''] + segmentation_label_names[1:].tolist()]
csv_header += [[''] +
[str(lab) for lab in segmentation_labels[1:]]]
else:
csv_header = [['subjects'] +
[str(lab) for lab in segmentation_labels[1:]]]
with open(path_volumes, 'w') as csvFile:
writer = csv.writer(csvFile)
writer.writerows(csv_header)
csvFile.close()
# build network
_, _, n_dims, n_channels, _, _ = utils.get_volume_info(path_images[0])
model_input_shape = [None] * n_dims + [n_channels]
net = build_model(path_model, model_input_shape, n_levels,
len(segmentation_labels), conv_size, nb_conv_per_level,
unet_feat_count, feat_multiplier, activation,
sigma_smoothing, gradients)
# perform segmentation
loop_info = utils.LoopInfo(len(path_images), 10, 'predicting', True)
for idx, (path_image, path_segmentation, path_posterior, path_resample, tmp_compute) in \
enumerate(zip(path_images, path_segmentations, path_posteriors, path_resampled, compute)):
# compute segmentation only if needed
if tmp_compute:
if verbose:
loop_info.update(idx)
# preprocessing
image, aff, h, im_res, _, _, shape, pad_shape, crop_idx, im_flipped = \
preprocess_image(path_image, n_levels, target_res, cropping, padding, flip, path_resample)
# prediction
prediction_patch = net.predict(image)
prediction_patch_flip = net.predict(im_flipped) if flip else None
# postprocessing
seg, posteriors = postprocess(
prediction_patch,
pad_shape,
shape,
crop_idx,
n_dims,
segmentation_labels,
lr_indices,
keep_biggest_component,
aff,
topology_classes=topology_classes,
post_patch_flip=prediction_patch_flip)
# write results to disk
if path_segmentation is not None:
utils.save_volume(seg,
aff,
h,
path_segmentation,
dtype='int32')
if path_posterior is not None:
if n_channels > 1:
posteriors = utils.add_axis(posteriors, axis=[0, -1])
utils.save_volume(posteriors,
aff,
h,
path_posterior,
dtype='float32')
else:
if path_volumes is not None:
posteriors, _, _, _, _, _, im_res = utils.get_volume_info(
path_posterior, True, aff_ref=np.eye(4))
else:
posteriors = im_res = None
# compute volumes
if path_volumes is not None:
volumes = np.sum(posteriors[..., 1:],
axis=tuple(range(0,
len(posteriors.shape) - 1)))
volumes = np.around(volumes * np.prod(im_res), 3)
row = [os.path.basename(path_image).replace('.nii.gz', '')
] + [str(vol) for vol in volumes]
with open(path_volumes, 'a') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(row)
csvFile.close()
# evaluate
if gt_folder is not None:
# find path where segmentations are saved evaluation folder, and get labels on which to evaluate
eval_folder = os.path.dirname(path_segmentations[0])
if evaluation_labels is None:
evaluation_labels = segmentation_labels
# set path of result arrays for surface distance if necessary
if compute_distances:
path_hausdorff = os.path.join(eval_folder, 'hausdorff.npy')
path_hausdorff_99 = os.path.join(eval_folder, 'hausdorff_99.npy')
path_hausdorff_95 = os.path.join(eval_folder, 'hausdorff_95.npy')
path_mean_distance = os.path.join(eval_folder, 'mean_distance.npy')
else:
path_hausdorff = path_hausdorff_99 = path_hausdorff_95 = path_mean_distance = None
# compute evaluation metrics
evaluate.evaluation(gt_folder,
eval_folder,
evaluation_labels,
mask_dir=mask_folder,
path_dice=os.path.join(eval_folder, 'dice.npy'),
path_hausdorff=path_hausdorff,
path_hausdorff_99=path_hausdorff_99,
path_hausdorff_95=path_hausdorff_95,
path_mean_distance=path_mean_distance,
list_incorrect_labels=list_incorrect_labels,
list_correct_labels=list_correct_labels,
recompute=recompute,
verbose=verbose)
def training(image_dir,
labels_dir,
cropping=None,
flipping=True,
scaling_range=0.07,
rotation_range=10,
shearing_range=0.01,
nonlin_std_dev=3,
nonlin_shape_fact=0.04,
crop_channel_2=None,
conv_size=3,
n_levels=5,
nb_conv_per_level=2,
feat_multiplier=2,
dropout=0,
unet_feat_count=24,
no_batch_norm=False,
lr=1e-4,
lr_decay=0,
batch_size=1,
wl2_epochs=50,
dice_epochs=500,
steps_per_epoch=100,
background_weight=1e-4,
include_background=False,
load_model_file=None,
initial_epoch_wl2=0,
initial_epoch_dice=0,
path_label_list=None,
model_dir=None):
# check epochs
assert (wl2_epochs > 0) | (dice_epochs > 0), \
'either wl2_epochs or dice_epochs must be positive, had {0} and {1}'.format(wl2_epochs, dice_epochs)
# prepare data files
image_paths = utils.list_images_in_folder(image_dir)
labels_paths = utils.list_images_in_folder(labels_dir)
assert len(image_paths) == len(labels_paths), "There should be as many images as label maps."
# get label and classes lists
rotation_range = utils.load_array_if_path(rotation_range)
scaling_range = utils.load_array_if_path(scaling_range)
crop_channel_2 = utils.load_array_if_path(crop_channel_2)
label_list, n_neutral_labels = utils.get_list_labels(label_list=path_label_list, FS_sort=True)
n_labels = np.size(label_list)
# prepare model folder
if not os.path.isdir(model_dir):
os.mkdir(model_dir)
# prepare log folder
log_dir = os.path.join(model_dir, 'logs')
if not os.path.isdir(log_dir):
os.mkdir(log_dir)
# create augmentation model and input generator
im_shape, aff, _, n_channels, _, _ = utils.get_volume_info(image_paths[0])
augmentation_model, unet_input_shape = labels_to_image_model(im_shape=im_shape,
n_channels=n_channels,
crop_shape=cropping,
label_list=label_list,
n_neutral_labels=n_neutral_labels,
vox2ras=aff,
nonlin_shape_factor=nonlin_shape_fact,
crop_channel2=crop_channel_2,
output_div_by_n=2 ** n_levels,
flipping=flipping)
model_input_generator = build_model_input_generator(images_paths=image_paths,
labels_paths=labels_paths,
n_channels=n_channels,
im_shape=im_shape,
scaling_range=scaling_range,
rotation_range=rotation_range,
shearing_range=shearing_range,
nonlin_shape_fact=nonlin_shape_fact,
nonlin_std_dev=nonlin_std_dev,
batch_size=batch_size)
training_generator = utils.build_training_generator(model_input_generator, batch_size)
# prepare the segmentation model
if no_batch_norm:
batch_norm_dim = None
else:
batch_norm_dim = -1
unet_model = nrn_models.unet(nb_features=unet_feat_count,
input_shape=unet_input_shape,
nb_levels=n_levels,
conv_size=conv_size,
nb_labels=n_labels,
feat_mult=feat_multiplier,
dilation_rate_mult=1,
nb_conv_per_level=nb_conv_per_level,
conv_dropout=dropout,
batch_norm=batch_norm_dim,
input_model=augmentation_model)
# pre-training with weighted L2, input is fit to the softmax rather than the probabilities
if wl2_epochs > 0:
wl2_model = Model(unet_model.inputs, [unet_model.get_layer('unet_likelihood').output])
wl2_model = metrics_model.metrics_model(input_shape=unet_input_shape[:-1] + [n_labels],
segmentation_label_list=label_list,
input_model=wl2_model,
metrics='weighted_l2',
weight_background=background_weight,
name='metrics_model')
if load_model_file is not None:
print('loading', load_model_file)
wl2_model.load_weights(load_model_file)
train_model(wl2_model, training_generator, lr, lr_decay, wl2_epochs, steps_per_epoch, model_dir, log_dir,
'wl2', initial_epoch_wl2)
# fine-tuning with dice metric
if dice_epochs > 0:
dice_model = metrics_model.metrics_model(input_shape=unet_input_shape[:-1] + [n_labels],
segmentation_label_list=label_list,
input_model=unet_model,
include_background=include_background,
name='metrics_model')
if wl2_epochs > 0:
last_wl2_model_name = os.path.join(model_dir, 'wl2_%03d.h5' % wl2_epochs)
dice_model.load_weights(last_wl2_model_name, by_name=True)
elif load_model_file is not None:
print('loading', load_model_file)
dice_model.load_weights(load_model_file)
train_model(dice_model, training_generator, lr, lr_decay, dice_epochs, steps_per_epoch, model_dir, log_dir,
'dice', initial_epoch_dice)
def __init__(self,
labels_dir,
generation_labels=None,
output_labels=None,
n_neutral_labels=None,
padding_margin=None,
batch_size=1,
n_channels=1,
target_res=None,
output_shape=None,
output_div_by_n=None,
prior_distributions='uniform',
generation_classes=None,
prior_means=None,
prior_stds=None,
use_specific_stats_for_channel=False,
flipping=True,
apply_linear_trans=True,
scaling_bounds=None,
rotation_bounds=None,
shearing_bounds=None,
apply_nonlin_trans=True,
nonlin_std=3.,
nonlin_shape_factor=0.0625,
blur_background=True,
data_res=None,
thickness=None,
downsample=False,
blur_range=1.15,
crop_channel_2=None,
apply_bias_field=True,
bias_field_std=0.3,
bias_shape_factor=0.025):
"""
This class is wrapper around the labels_to_image_model model. It contains the GPU model that generates images
from labels maps, and a python generator that suplies the input data for this model.
To generate pairs of image/labels you can just call the method generate_image() on an object of this class.
:param labels_dir: path of folder with all input label maps, or to a single label map.
# IMPORTANT !!!
# Each time we provide a parameter with separate values for each axis (e.g. with a numpy array or a sequence),
# these values refer to the axes of the raw label map (i.e. once it has been loaded in python).
# Depending on the label map orientation, the axes of its raw array may or may not correspond to the RAS axes.
# label maps-related parameters
:param generation_labels: (optional) list of all possible label values in the input label maps.
Default is None, where the label values are directly gotten from the provided label maps.
If not None, can be a sequence or a 1d numpy array, or the path to a 1d numpy array.
If flipping is true (i.e. right/left flipping is enabled), generation_labels should be organised as follows:
background label first, then non-sided labels (e.g. CSF, brainstem, etc.), then all the structures of the same
hemisphere (can be left or right), and finally all the corresponding contralateral structures in the same order.
:param output_labels: (optional) list of all the label values to keep in the output label maps (in no particular
order). Should be a subset of the values contained in generation_labels.
Label values that are in generation_labels but not in output_labels are reset to zero.
Can be a sequence, a 1d numpy array, or the path to a 1d numpy array.
:param n_neutral_labels: (optional) number of non-sided generation labels.
Default is total number of label values.
:param padding_margin: (optional) margin by which to pad the input labels with zeros.
Padding is applied prior to any other operation.
Can be an integer (same padding in all dimensions), a sequence, a 1d numpy array, or the path to a 1d numpy
array. Default is no padding.
# output-related parameters
:param batch_size: (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 target_res: (optional) target resolution of the generated images and corresponding label maps.
If None, the outputs will have the same resolution as the input label maps.
Can be a number (isotropic resolution), a sequence, a 1d numpy array, or the path to a 1d numpy array.
:param output_shape: (optional) shape of the output image, obtained by randomly cropping the generated image.
Can be an integer (same size in all dimensions), a sequence, a 1d numpy array, or the path to a 1d numpy array.
:param output_div_by_n: (optional) forces the output shape to be divisible by this value. It overwrites
output_shape if necessary. Can be an integer (same size in all dimensions), a sequence, a 1d numpy array, or
the path to a 1d numpy array.
# GMM-sampling parameters
: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, a
1d numpy array, or the path to 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 (K=len(generation_labels)).
: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, and 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.
# spatial deformation parameters
:param flipping: (optional) whether to introduce right/left random flipping. Default is True.
: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.
4) the path to such a numpy array.
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 apply_nonlin_trans: (optional) whether to apply non linear elastic deformation.
If true, a diffeomorphic deformation field is obtained by first sampling a small tensor from the normal
distribution, resizing it to image size, and integrationg it. Default is True.
:param nonlin_std: (optional) If apply_nonlin_trans is True, maximum value for the standard deviation of the
normal distribution from which we sample the first tensor for synthesising the deformation field.
:param nonlin_shape_factor: (optional) If apply_nonlin_trans is True, ratio between the size of the input label
maps and the size of the sampled tensor for synthesising the deformation field.
# blurring/resampling parameters
:param blur_background: (optional) whether to produce an unrealistic background or not.
If True, the background is generated/blurred with the other labels, according to the values of prior_means and
prior_stds. Also, it is reset to zero-background with a probability of 0.2.
If False, the background is reset to zero, or can be replaced by a low-intensity background with a probability
of 0.5. Additionally we correct for edge blurring effects.
Default is True.
:param data_res: (optional) acquisition resolution to mimick. If provided, the images sampled from the GMM are
blurred to mimick data that would be: 1) acquired at the given acquisition resolution, and 2) resample at
target_resolution.
Default is None, where images are isotropically blurred to introduce some spatial correlation between voxels.
If the generated images are uni-modal, data_res can be a number (isotropic acquisition resolution), a sequence,
a 1d numpy array, or the path to a 1d numy array. In the multi-modal case, it should be given as a numpy array
(or a path) of size (n_mod, n_dims), where each row is the acquisition resolution of the correspionding chanel.
:param thickness: (optional) if data_res is provided, we can further specify the slice thickness of the low
resolution images to mimick.
If the generated images are uni-modal, data_res can be a number (isotropic acquisition resolution), a sequence,
a 1d numpy array, or the path to a 1d numy array. In the multi-modal case, it should be given as a numpy array
(or a path) of size (n_mod, n_dims), where each row is the acquisition resolution of the correspionding chanel.
:param downsample: (optional) whether to actually downsample the volume image to data_res.
Default is False, except when thickness is provided, and thickness < data_res.
:param blur_range: (optional) Randomise the standard deviation of the blurring kernels, (whether data_res is
given or not). At each mini_batch, the standard deviation of the blurring kernels are multiplied by a
coefficient sampled from a uniform distribution with bounds [1/blur_range, blur_range].
If None, no randomisation. Default is 1.15.
:param crop_channel_2: (optional) stats for cropping second channel along the anterior-posterior axis.
Should be a vector of length 4, with bounds of uniform distribution for cropping the front and back of the image
(in percentage). None is no croppping.
# bias field parameters
:param apply_bias_field: (optional) whether to apply a bias field to the final image. Default is True.
If True, the bias field is obtained by sampling a first tensor from normal distribution, resizing it to image
size, and rescaling the values to positive number by taking the voxel-wise exponential. Default is True.
:param bias_field_std: (optional) If apply_nonlin_trans is True, maximum value for the standard deviation of the
normal distribution from which we sample the first tensor for synthesising the bias field.
:param bias_shape_factor: (optional) If apply_bias_field is True, ratio between the size of the input
label maps and the size of the sampled tensor for synthesising the bias field.
"""
# prepare data files
if ('.nii.gz' in labels_dir) | ('.nii' in labels_dir) | (
'.mgz' in labels_dir) | ('.npz' in labels_dir):
self.labels_paths = [labels_dir]
else:
self.labels_paths = utils.list_images_in_folder(labels_dir)
assert len(self.labels_paths) > 0, "Could not find any training data"
# generation parameters
self.labels_shape, self.aff, self.n_dims, _, self.header, self.atlas_res = \
utils.get_volume_info(self.labels_paths[0])
self.n_channels = n_channels
if generation_labels is not None:
self.generation_labels = utils.load_array_if_path(
generation_labels)
else:
self.generation_labels = utils.get_list_labels(
labels_dir=labels_dir)
if output_labels is not None:
self.output_labels = utils.load_array_if_path(output_labels)
else:
self.output_labels = self.generation_labels
if n_neutral_labels is not None:
self.n_neutral_labels = n_neutral_labels
else:
self.n_neutral_labels = self.generation_labels.shape[0]
self.target_res = utils.load_array_if_path(target_res)
# preliminary operations
self.padding_margin = utils.load_array_if_path(padding_margin)
self.flipping = flipping
self.output_shape = utils.load_array_if_path(output_shape)
self.output_div_by_n = output_div_by_n
# GMM parameters
self.prior_distributions = prior_distributions
if generation_classes is not None:
self.generation_classes = utils.load_array_if_path(
generation_classes)
assert self.generation_classes.shape == self.generation_labels.shape, \
'if provided, generation labels should have the same shape as generation_labels'
unique_classes = np.unique(self.generation_classes)
assert np.array_equal(unique_classes, np.arange(np.max(unique_classes)+1)), \
'generation_classes should a linear range between 0 and its maximum value.'
else:
self.generation_classes = np.arange(
self.generation_labels.shape[0])
self.prior_means = utils.load_array_if_path(prior_means)
self.prior_stds = utils.load_array_if_path(prior_stds)
self.use_specific_stats_for_channel = use_specific_stats_for_channel
# linear transformation parameters
self.apply_linear_trans = apply_linear_trans
self.scaling_bounds = utils.load_array_if_path(scaling_bounds)
self.rotation_bounds = utils.load_array_if_path(rotation_bounds)
self.shearing_bounds = utils.load_array_if_path(shearing_bounds)
# elastic transformation parameters
self.apply_nonlin_trans = apply_nonlin_trans
self.nonlin_std = nonlin_std
self.nonlin_shape_factor = nonlin_shape_factor
# blurring parameters
self.blur_background = blur_background
self.data_res = utils.load_array_if_path(data_res)
self.thickness = utils.load_array_if_path(thickness)
self.downsample = downsample
self.blur_range = blur_range
self.crop_second_channel = utils.load_array_if_path(crop_channel_2)
# bias field parameters
self.apply_bias_field = apply_bias_field
self.bias_field_std = bias_field_std
self.bias_shape_factor = bias_shape_factor
# build transformation model
self.labels_to_image_model, self.model_output_shape = self._build_labels_to_image_model(
)
# build generator for model inputs
self.model_inputs_generator = self._build_model_inputs_generator(
batch_size)
# build brain generator
self.brain_generator = self._build_brain_generator()