nn

Title : Denoising diffusion weighted imaging data using CNN

Obtaining tissue microstructure measurements from diffusion weighted imaging (DWI) with multiple, high b-values is crucial. However, the high noise levels present in these images can adversely affect the accuracy of the microstructural measurements. In this context, we suggest a straightforward denoising technique that can be applied to any DWI dataset as long as a low-noise, single-subject dataset is obtained using the same DWI sequence.

We created a simple 1D-CNN model with five layers, based on the 1D CNN for denoising speech. The model consists of two convolutional layers followed by max-pooling layers, and a dense layer. The first convolutional layer has 16 one-dimensional filters of size 16, and the second layer has 32 filters of size 8. ReLu activation function is applied to both convolutional layers. The max-pooling layer has a kernel size of 2 and a stride of 2. The dense layer maps the features extracted from the noisy image to the low-noise reference image.

Cnn1DDenoiser

class dipy.nn.cnn_1d_denoising.Cnn1DDenoiser(sig_length, optimizer='adam', loss='mean_squared_error', metrics=('accuracy',), loss_weights=None)

Bases: object

__init__(sig_length, optimizer='adam', loss='mean_squared_error', metrics=('accuracy',), loss_weights=None)

Initialize the CNN 1D denoiser with the given parameters.

Parameters

sig_lengthint

Length of the DWI signal.

optimizerstr, optional

Name of the optimization algorithm to use. Options: ‘adam’, ‘sgd’, ‘rmsprop’, ‘adagrad’, ‘adadelta’.

lossstr, optional

Name of the loss function to use. Available options are ‘mean_squared_error’, ‘mean_absolute_error’, ‘mean_absolute_percentage_error’, ‘mean_squared_logarithmic_error’, ‘squared_hinge’, ‘hinge’, ‘categorical_hinge’, ‘logcosh’, ‘categorical_crossentropy’, ‘sparse_categorical_crossentropy’, ‘binary_crossentropy’, ‘kullback_leibler_divergence’, ‘poisson’, ‘cosine_similarity’. Suggested to go with ‘mean_squared_error’.

metricstuple of str or function, optional

List of metrics to be evaluated by the model during training and testing. Available options are ‘accuracy’, ‘binary_accuracy’, ‘categorical_accuracy’, ‘top_k_categorical_accuracy’, ‘sparse_categorical_accuracy’, ‘sparse_top_k_categorical_accuracy’, and any custom function.

loss_weightsfloat or dict, optional

Scalar coefficients to weight the loss contributions of different model outputs. Can be a single float value or a dictionary mapping output names to scalar coefficients.

compile(optimizer='adam', loss=None, metrics=None, loss_weights=None)

Configure the model for training.

Parameters

optimizerstr or optimizer object, optional

Name of optimizer or optimizer object.

lossstr or objective function, optional

Name of objective function or objective function itself. If ‘None’, the model will be compiled without any loss function and can only be used to predict output.

metricslist of metrics, optional

List of metrics to be evaluated by the model during training and testing.

loss_weightslist or dict, optional

Optional list or dictionary specifying scalar coefficients(floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses. If a list, it is expected to have a 1:1 mapping to the model’s outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.

evaluate(x, y, batch_size=None, verbose=1, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, return_dict=False)

Evaluate the model on a test dataset.

Parameters

xndarray

Test dataset (high-noise data). If 4D, it will be converted to 1D.

yndarray

Labels of the test dataset (low-noise data). If 4D, it will be converted to 1D.

batch_sizeint, optional

Number of samples per gradient update.

verboseint, optional

Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.

stepsint, optional

Total number of steps (batches of samples) before declaring the evaluation round finished.

callbackslist, optional

List of callbacks to apply during evaluation.

max_queue_sizeint, optional

Maximum size for the generator queue.

workersint, optional

Maximum number of processes to spin up when using process-based threading.

use_multiprocessingbool, optional

If True, use process-based threading.

return_dictbool, optional

If True, loss and metric results are returned as a dictionary.

Returns

List or dict

If return_dict is False, returns a list of [loss, metrics] values on the test dataset. If return_dict is True, returns a dictionary of metric names and their corresponding values.

fit(x, y, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False)

Train the model on train dataset.

The fit method will train the model for a fixed number of epochs (iterations) on a dataset. If given data is 4D it will convert it into 1D.

Parameters

xndarray

The input data, as an ndarray.

yndarray

The target data, as an ndarray.

batch_sizeint or None, optional

Number of samples per batch of computation.

epochsint, optional

The number of epochs.

verbose‘auto’, 0, 1, or 2, optional

Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.

callbackslist of keras.callbacks.Callback instances, optional

List of callbacks to apply during training.

validation_splitfloat between 0 and 1, optional

Fraction of the training data to be used as validation data.

validation_datatuple (x_val, y_val) or None, optional

Data on which to evaluate the loss and any model metrics at the end of each epoch.

shuffleboolean, optional

This argument is ignored when x is a generator or an object of tf.data.Dataset.

initial_epochint, optional

Epoch at which to start training.

steps_per_epochint or None, optional

Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch.

validation_batch_sizeint or None, optional

Number of samples per validation batch.

validation_stepsint or None, optional

Only relevant if validation_data is provided and is a tf.data dataset.

validation_freqint or list/tuple/set, optional

Only relevant if validation data is provided. If an integer, specifies how many training epochs to run before a new validation run is performed. If a list, tuple, or set, specifies the epochs on which to run validation.

max_queue_sizeint, optional

Used for generator or keras.utils.Sequence input only.

workersinteger, optional

Used for generator or keras.utils.Sequence input only.

use_multiprocessingboolean, optional

Used for generator or keras.utils.Sequence input only.

Returns

histobject

A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs.

load_weights(filepath)

Load the model weights from the specified file path.

Parameters

filepathstr

The file path from which to load the weights.

predict(x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)

Generate predictions for input samples.

Parameters

xndarray

Input samples.

batch_sizeint, optional

Number of samples per batch.

verboseint, optional

Verbosity mode.

stepsint, optional

Total number of steps (batches of samples) before declaring the prediction round finished.

callbackslist, optional

List of Keras callbacks to apply during prediction.

max_queue_sizeint, optional

Maximum size for the generator queue.

workersint, optional

Maximum number of processes to spin up when using process-based threading.

use_multiprocessingbool, optional

If True, use process-based threading. If False, use thread-based threading.

Returns

ndarray

Numpy array of predictions.

save_weights(filepath, overwrite=True)

Save the weights of the model to HDF5 file format.

Parameters

filepathstr

The path where the weights should be saved.

overwritebool,optional

If True, overwrites the file if it already exists. If False, raises an error if the file already exists.

summary()

Get the summary of the model.

The summary is textual and includes information about: The layers and their order in the model. The output shape of each layer.

Returns

summaryNoneType

the summary of the model

train_test_split(x, y, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)

Split the input data into random train and test subsets.

Parameters

x: numpy array

input data.

y: numpy array

target data.

test_size: float or int, optional

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25.

train_size: float or int, optional

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

random_state: int, RandomState instance or None, optional

Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. See Glossary.

shuffle: bool, optional

Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.

stratify: array-like, optional

If not None, data is split in a stratified fashion, using this as the class labels. Read more in the User Guide.

Returns

Tuple of four numpy arrays: x_train, x_test, y_train, y_test.

Block

class dipy.nn.evac.Block(*args, **kwargs)

Bases: Layer

__init__(out_channels, kernel_size, strides, padding, drop_r, n_layers, layer_type='down')

call(input, passed)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

ChannelSum

class dipy.nn.evac.ChannelSum(*args, **kwargs)

Bases: Layer

__init__()

call(inputs)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

EVACPlus

class dipy.nn.evac.EVACPlus(verbose=False)

Bases: object

This class is intended for the EVAC+ model.

__init__(verbose=False)

The model was pre-trained for usage on brain extraction of T1 images.

This model is designed to take as input a T1 weighted image.

Parameters

verbosebool (optional)

Whether to show information about the processing. Default: False

References

fetch_default_weights()

Load the model pre-training weights to use for the fitting. While the user can load different weights, the function is mainly intended for the class function ‘predict’.

load_model_weights(weights_path)

Load the custom pre-training weights to use for the fitting.

Parameters

weights_pathstr

Path to the file containing the weights (hdf5, saved by tensorflow)

predict(T1, affine, voxsize=(1, 1, 1), batch_size=None, return_affine=False, return_prob=False)

Wrapper function to facilitate prediction of larger dataset.

Parameters

T1np.ndarray or list of np.ndarrys

For a single image, input should be a 3D array. If multiple images, it should be a 4D array or a list.

affinenp.ndarray (4, 4) or (batch, 4, 4)

or list of np.ndarrays with len of batch Affine matrix for the T1 image. Should have batch dimension if T1 has one.

voxsizenp.ndarray or list or tuple, optional

(3,) or (batch, 3) voxel size of the T1 image. Default is (1, 1, 1)

batch_sizeint, optional

Number of images per prediction pass. Only available if data is provided with a batch dimension. Consider lowering it if you get an out of memory error. Increase it if you want it to be faster and have a lot of data. If None, batch_size will be set to 1 if the provided image has a batch dimension. Default is None

return_affinebool, optional

Whether to return the affine matrix. Useful if the input was a file path. Default is False

return_probbool, optional

Whether to return the probability map instead of a binary mask. Useful for testing. Default is False

Returns

pred_outputnp.ndarray (…) or (batch, …)

Predicted brain mask

affinenp.ndarray (…) or (batch, …)

affine matrix of mask only if return_affine is True

logger

dipy.nn.evac.logger()

Instances of the Logger class represent a single logging channel. A “logging channel” indicates an area of an application. Exactly how an “area” is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of “input processing” might include sub-areas “read CSV files”, “read XLS files” and “read Gnumeric files”). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be “input” for the upper level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels. There is no arbitrary limit to the depth of nesting.

prepare_img

dipy.nn.evac.prepare_img(image)

Function to prepare image for model input Specific to EVAC+

Parameters

imagenp.ndarray

Input image

Returns

input_data : dict

init_model

dipy.nn.evac.init_model(model_scale=16)

Function to create model for EVAC+

Parameters

model_scaleint, optional

The scale of the model Should match the saved weights from fetcher Default is 16

Returns

model : tf.keras.Model

HistoResDNN

class dipy.nn.histo_resdnn.HistoResDNN(sh_order=8, basis_type='tournier07', verbose=False)

Bases: object

This class is intended for the ResDNN Histology Network model.

__init__(sh_order=8, basis_type='tournier07', verbose=False)

The model was re-trained for usage with a different basis function (‘tournier07’) like the proposed model in [1, 2].

To obtain the pre-trained model, use:: >>> resdnn_model = HistoResDNN() >>> fetch_model_weights_path = get_fnames(‘histo_resdnn_weights’) >>> resdnn_model.load_model_weights(fetch_model_weights_path)

This model is designed to take as input raw DWI signal on a sphere (ODF) represented as SH of order 8 in the tournier basis and predict fODF of order 8 in the tournier basis. Effectively, this model is mimicking a CSD fit.

Parameters

sh_orderint, optional

Maximum SH order in the SH fit. For sh_order, there will be (sh_order + 1) * (sh_order + 2) / 2 SH coefficients for a symmetric basis. Default: 8

basis_type{‘tournier07’, ‘descoteaux07’}, optional

tournier07 (default) or descoteaux07.

verbosebool (optional)

Whether to show information about the processing. Default: False

References

fetch_default_weights()

Load the model pre-training weights to use for the fitting. Will not work if the declared SH_ORDER does not match the weights expected input.

load_model_weights(weights_path)

Load the custom pre-training weights to use for the fitting. Will not work if the declared SH_ORDER does not match the weights expected input.

The weights for a sh_order of 8 can be obtained via the function:

get_fnames(‘histo_resdnn_weights’).

Parameters

weights_pathstr

Path to the file containing the weights (hdf5, saved by tensorflow)

predict(data, gtab, mask=None, chunk_size=1000)

Wrapper function to facilitate prediction of larger dataset. The function will mask, normalize, split, predict and ‘re-assemble’ the data as a volume.

Parameters

datanp.ndarray

DWI signal in a 4D array

gtabGradientTable class instance

The acquisition scheme matching the data (must contain at least one b0)

masknp.ndarray (optional)

Binary mask of the brain to avoid unnecessary computation and unreliable prediction outside the brain. Default: Compute prediction only for nonzero voxels (with at least one nonzero DWI value).

Returns

pred_sh_coefnp.ndarray (x, y, z, M)

Predicted fODF (as SH). The volume has matching shape to the input data, but with (sh_order + 1) * (sh_order + 2) / 2 as a last dimension.

logger

dipy.nn.histo_resdnn.logger()

Instances of the Logger class represent a single logging channel. A “logging channel” indicates an area of an application. Exactly how an “area” is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of “input processing” might include sub-areas “read CSV files”, “read XLS files” and “read Gnumeric files”). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be “input” for the upper level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels. There is no arbitrary limit to the depth of nesting.

SingleLayerPerceptron

class dipy.nn.model.SingleLayerPerceptron(input_shape=(28, 28), num_hidden=128, act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', optimizer='adam', loss='sparse_categorical_crossentropy')

Bases: object

__init__(input_shape=(28, 28), num_hidden=128, act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', optimizer='adam', loss='sparse_categorical_crossentropy')

Single Layer Perceptron with Dropout.

Parameters

input_shapetuple

Shape of data to be trained

num_hiddenint

Number of nodes in hidden layer

act_hiddenstring

Activation function used in hidden layer

dropoutfloat

Dropout ratio

num_out10

Number of nodes in output layer

act_outstring

Activation function used in output layer

optimizerstring

Select optimizer. Default adam.

lossstring

Select loss function for measuring accuracy. Default sparse_categorical_crossentropy.

evaluate(x_test, y_test, verbose=2)

Evaluate the model on test dataset.

The evaluate method will evaluate the model on a test dataset.

Parameters

x_testndarray

the x_test is the test dataset

y_testndarray shape=(BatchSize,)

the y_test is the labels of the test dataset

verboseint (Default = 2)

By setting verbose 0, 1 or 2 you just say how do you want to ‘see’ the training progress for each epoch.

Returns

evaluateList

return list of loss value and accuracy value on test dataset

fit(x_train, y_train, epochs=5)

Train the model on train dataset.

The fit method will train the model for a fixed number of epochs (iterations) on a dataset.

Parameters

x_trainndarray

the x_train is the train dataset

y_trainndarray shape=(BatchSize,)

the y_train is the labels of the train dataset

epochsint (Default = 5)

the number of epochs

Returns

histobject

A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs

predict(x_test)

Predict the output from input samples.

The predict method will generates output predictions for the input samples.

Parameters

x_trainndarray

the x_test is the test dataset or input samples

Returns

predictndarray shape(TestSize,OutputSize)

Numpy array(s) of predictions.

summary()

Get the summary of the model.

The summary is textual and includes information about: The layers and their order in the model. The output shape of each layer.

Returns

summaryNoneType

the summary of the model

MultipleLayerPercepton

class dipy.nn.model.MultipleLayerPercepton(input_shape=(28, 28), num_hidden=(128,), act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', loss='sparse_categorical_crossentropy', optimizer='adam')

Bases: object

__init__(input_shape=(28, 28), num_hidden=(128,), act_hidden='relu', dropout=0.2, num_out=10, act_out='softmax', loss='sparse_categorical_crossentropy', optimizer='adam')

Multiple Layer Perceptron with Dropout.

Parameters

input_shapetuple

Shape of data to be trained

num_hiddenarray-like

List of number of nodes in hidden layers

act_hiddenstring

Activation function used in hidden layer

dropoutfloat

Dropout ratio

num_out10

Number of nodes in output layer

act_outstring

Activation function used in output layer

optimizerstring

Select optimizer. Default adam.

lossstring

Select loss function for measuring accuracy. Default sparse_categorical_crossentropy.

evaluate(x_test, y_test, verbose=2)

Evaluate the model on test dataset.

The evaluate method will evaluate the model on a test dataset.

Parameters

x_testndarray

the x_test is the test dataset

y_testndarray shape=(BatchSize,)

the y_test is the labels of the test dataset

verboseint (Default = 2)

By setting verbose 0, 1 or 2 you just say how do you want to ‘see’ the training progress for each epoch.

Returns

evaluateList

return list of loss value and accuracy value on test dataset

fit(x_train, y_train, epochs=5)

Train the model on train dataset.

The fit method will train the model for a fixed number of epochs (iterations) on a dataset.

Parameters

x_trainndarray

the x_train is the train dataset

y_trainndarray shape=(BatchSize,)

the y_train is the labels of the train dataset

epochsint (Default = 5)

the number of epochs

Returns

histobject

A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs

predict(x_test)

Predict the output from input samples.

The predict method will generates output predictions for the input samples.

Parameters

x_trainndarray

the x_test is the test dataset or input samples

Returns

predictndarray shape(TestSize,OutputSize)

Numpy array(s) of predictions.

summary()

Get the summary of the model.

The summary is textual and includes information about: The layers and their order in the model. The output shape of each layer.

Returns

summaryNoneType

the summary of the model

EncoderBlock

class dipy.nn.synb0.EncoderBlock(*args, **kwargs)

Bases: Layer

__init__(out_channels, kernel_size, strides, padding)

call(input)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

DecoderBlock

class dipy.nn.synb0.DecoderBlock(*args, **kwargs)

Bases: Layer

__init__(out_channels, kernel_size, strides, padding)

call(input)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

EncoderBlock

class dipy.nn.synb0.EncoderBlock(*args, **kwargs)

Bases: Layer

__init__(out_channels, kernel_size, strides, padding)

call(input)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

DecoderBlock

class dipy.nn.synb0.DecoderBlock(*args, **kwargs)

Bases: Layer

__init__(out_channels, kernel_size, strides, padding)

call(input)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

Synb0

class dipy.nn.synb0.Synb0(verbose=False)

Bases: object

This class is intended for the Synb0 model. The model is the deep learning part of the Synb0-Disco pipeline, thus stand-alone usage is not recommended.

__init__(verbose=False)

The model was pre-trained for usage on pre-processed images following the synb0-disco pipeline. One can load their own weights using load_model_weights.

This model is designed to take as input a b0 image and a T1 weighted image.

It was designed to predict a b-inf image.

Parameters

verbosebool (optional)

Whether to show information about the processing. Default: False

References

fetch_default_weights(idx)

Load the model pre-training weights to use for the fitting. While the user can load different weights, the function is mainly intended for the class function ‘predict’.

Parameters

idxint

The idx of the default weights. It can be from 0~4.

load_model_weights(weights_path)

Load the custom pre-training weights to use for the fitting.

Parameters

weights_pathstr

Path to the file containing the weights (hdf5, saved by tensorflow)

predict(b0, T1, batch_size=None, average=True)

Wrapper function to facilitate prediction of larger dataset. The function will pad the data to meet the required shape of image. Note that the b0 and T1 image should have the same shape

Parameters

b0np.ndarray (batch, 77, 91, 77) or (77, 91, 77)

For a single image, input should be a 3D array. If multiple images, there should also be a batch dimension.

T1np.ndarray (batch, 77, 91, 77) or (77, 91, 77)

For a single image, input should be a 3D array. If multiple images, there should also be a batch dimension.

batch_sizeint

Number of images per prediction pass. Only available if data is provided with a batch dimension. Consider lowering it if you get an out of memory error. Increase it if you want it to be faster and have a lot of data. If None, batch_size will be set to 1 if the provided image has a batch dimension. Default is None

averagebool

Whether the function follows the Synb0-Disco pipeline and averages the prediction of 5 different models. If False, it uses the loaded weights for prediction. Default is True.

Returns

pred_outputnp.ndarray (…) or (batch, …)

Reconstructed b-inf image(s)

logger

dipy.nn.synb0.logger()

Instances of the Logger class represent a single logging channel. A “logging channel” indicates an area of an application. Exactly how an “area” is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of “input processing” might include sub-areas “read CSV files”, “read XLS files” and “read Gnumeric files”). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be “input” for the upper level, and “input.csv”, “input.xls” and “input.gnu” for the sub-levels. There is no arbitrary limit to the depth of nesting.

UNet3D

dipy.nn.synb0.UNet3D(input_shape)

normalize

dipy.nn.synb0.normalize(image, min_v=None, max_v=None, new_min=-1, new_max=1)

normalization function

Parameters

image : np.ndarray min_v : int or float (optional)

minimum value range for normalization intensities below min_v will be clipped if None it is set to min value of image Default : None

max_vint or float (optional)

maximum value range for normalization intensities above max_v will be clipped if None it is set to max value of image Default : None

new_minint or float (optional)

new minimum value after normalization Default : 0

new_maxint or float (optional)

new maximum value after normalization Default : 1

Returns

np.ndarray

Normalized image from range new_min to new_max

unnormalize

dipy.nn.synb0.unnormalize(image, norm_min, norm_max, min_v, max_v)

unnormalization function

Parameters

image : np.ndarray norm_min : int or float

minimum value of normalized image

norm_maxint or float

maximum value of normalized image

min_vint or float

minimum value of unnormalized image

max_vint or float

maximum value of unnormalized image

Returns

np.ndarray

unnormalized image from range min_v to max_v

UNet3D

dipy.nn.synb0.UNet3D(input_shape)

normalize

dipy.nn.utils.normalize(image, min_v=None, max_v=None, new_min=-1, new_max=1)

normalization function

Parameters

image : np.ndarray min_v : int or float (optional)

minimum value range for normalization intensities below min_v will be clipped if None it is set to min value of image Default : None

max_vint or float (optional)

maximum value range for normalization intensities above max_v will be clipped if None it is set to max value of image Default : None

new_minint or float (optional)

new minimum value after normalization Default : 0

new_maxint or float (optional)

new maximum value after normalization Default : 1

Returns

np.ndarray

Normalized image from range new_min to new_max

unnormalize

dipy.nn.utils.unnormalize(image, norm_min, norm_max, min_v, max_v)

unnormalization function

Parameters

image : np.ndarray norm_min : int or float

minimum value of normalized image

norm_maxint or float

maximum value of normalized image

min_vint or float

minimum value of unnormalized image

max_vint or float

maximum value of unnormalized image

Returns

np.ndarray

unnormalized image from range min_v to max_v

set_logger_level

dipy.nn.utils.set_logger_level(log_level, logger)

Change the logger to one of the following: DEBUG, INFO, WARNING, CRITICAL, ERROR

Parameters

log_levelstr

Log level for the logger

transform_img

dipy.nn.utils.transform_img(image, affine, init_shape=(256, 256, 256), scale=2)

Function to reshape image as an input to the model

Parameters

imagenp.ndarray

Image to transform to voxelspace

affinenp.ndarray

Affine matrix provided by the file

init_shapetuple

Initial shape to transform the image to Default is (256, 256, 256)

scalefloat

How much we want to scale the image Default is 2

Returns

transformed_img : np.ndarray

recover_img

dipy.nn.utils.recover_img(image, affine, ori_shape, scale=2)

Function to recover image back to its original shape

Parameters

imagenp.ndarray

Image to recover

affinenp.ndarray

Affine matrix provided from transform_img

ori_shapetuple

Original shape of image

scalefloat

Scale that was used in transform_img Default is 2

Returns

recovered_img : np.ndarray