MRAI Convolutional Neural Network¶

class mrainet.mraicnn.MRAIConvolutionalNeuralNetwork(patch_size=(31, 31), classes=[1, 2, 3], num_draw=10, num_kernels=[8], kernel_size=[(3, 3)], dense_size=[16, 8], strides=(1, 1), dropout=0.1, l2=0.001, margin=1, optimizer='rmsprop', batch_size=32, num_epochs=1)¶

Network for MRI-scanner acquisition-invariant representation learning.

Class of convolutional neural networks that aim to map patches of two datasets from different MRI-scanners Methods include image processing operations, pair sampling and Siamese loss minimization.

Methods

`compile_net`(self)	Compile network architecture.
`contrastive_loss`(self, label, distance)	Contrastive Siamese loss.
`extract_random_patches`(self, X, Y)	Extract a random set of patches from image.
`feedforward`(self, patches, scan_ID)	Feed a set of patches forward through the network.
`gen_index_combs`(self, x)	Generate combinations of two index arrays.
`index2patch`(self, X, index)	Slice patches from an image at given indices.
`l1_norm`(self, x)	l1-norm for loss layer.
`l2_norm`(self, x)	l2-norm for loss layer.
`load_model`(self, model_fn, weights_fn)	Load model from filename.
`matrix2sparse`(self, X[, edge, remove_nans])	Map matrix to a sparse array format.
`sample_pairs`(self, X, y, Z, u[, num_draw])	Sample a set of pairs of patches from two images.
`save_model`(self, model_fn, weights_fn)	Save model to filename.
`segment_image`(self, X, model[, feed, …])	Segment a new image using the trained network.
`subsample_rows`(self, X[, num_draw])	Take a random subsample of rows from X.
`train`(self, X, Y, Z, U[, num_targets])	Train the network using pairs of patches from the images.

compile_net(self)¶: Compile network architecture.

contrastive_loss(self, label, distance)¶

Contrastive Siamese loss.

For similar pairs, it consists of the squared Lp-distance. For dissimilar pairs, it consists of a hinge loss with respect to a margin parameter.

Parameters:	label : int Similarity label, 1=similar and 0=dissimilar distance: float Lp-norm between pairs of patches mapped through the network.
Returns:	float Loss value for current pair of patches.

extract_random_patches(self, X, Y)¶

Extract a random set of patches from image.

Parameters:	X : array Input image to sample patches from. Y : array Label image corresponding to X.
Returns:	patches : array Patches array, num patches by patch height by patch width by 1. labels : array Tissue label array corresponding to patches array.

feedforward(self, patches, scan_ID)¶

Feed a set of patches forward through the network.

Parameters:	patches : array Contains patches in form of number of patches by patch height by patch width by 1. scan_ID : int Scanner identification variable, indicating from which MRI-scanner these patches came from.
Returns:	array Final layer representation of patches fed forward through the network.

gen_index_combs(self, x)¶: Generate combinations of two index arrays.

index2patch(self, X, index)¶

Slice patches from an image at given indices.

Parameters:	X : array input image index : array Row and column indices for the provided image.
Returns:	patches : array Number of patches by patch height by patch width by 1.

l1_norm(self, x)¶: l1-norm for loss layer.

l2_norm(self, x)¶: l2-norm for loss layer.

load_model(self, model_fn, weights_fn)¶

Load model from filename.

Parameters:	model_fn : str Filename of saved model. weights_fn : str Filename of saved weight matrix.
Returns:	None

matrix2sparse(self, X, edge=(0, 0), remove_nans=False)¶

Map matrix to a sparse array format.

Parameters:	X : array Matrix that should be mapped to sparse array format, may contain NaN’s. edge : tuple(int, int) Dimensions of edge to ignore. remove_nans : bool Whether to remove NaN’s as tissue labels.
Returns:	sX : array Original matrix mapped to (i,j,v) format where i corresponds to the i-th row of X, j to the j-column of X and v of the value at position (i,j) of X.

sample_pairs(self, X, y, Z, u, num_draw=(10, 1))¶

Sample a set of pairs of patches from two images.

Parameters:

X : array: slice from source MRI-scanner
y : array: source tissue index sparse array; where each row i,j,k consists of the pixel’s row index i, the pixel’s column index j and the pixel’s tissue k.
Z : array: slice from target MRI-scanner
u : array: target tissue index sparse array; where each row i,j,k consists of the pixel’s row index i, the pixel’s column index j and the pixel’s tissue k.
num_draw : tuple(int, int): maximum number of patches to draw from (source, target)

Returns:

P : list[A, B, a, b]: contains pairs of patches and scanner identifications
S : array: contains similarity labels between pairs

save_model(self, model_fn, weights_fn)¶

Save model to filename.

Parameters:	model_fn : str Filename to save model to. weights_fn : str Filename to save weight matrix to.
Returns:	None

segment_image(self, X, model, feed=True, mapost=False, scan_ID=1)¶

Segment a new image using the trained network.

Parameters:

X : array: new image that needs to be segmented.
model : sklearn-model: Trained classifier from scikit-learn, needs to have a predict method.
feed : bool: whether the extracted patches should be fed through the network, a value of False is for experimental purposes (def: True)
mapost : bool: whether to map the predictions to a maximum a posteriori form. (def: False)
scan_ID : int: scanner identification of new image.

Returns:

preds : array: Label image of same size as input image, containing predictions made by the provided trained classifier.

subsample_rows(self, X, num_draw=1)¶

Take a random subsample of rows from X.

Parameters:	X : array Array to subsample from. num_draw : int Number of rows to subsample. replace : bool Whether to replace sampled rows.
Returns:	array Smaller array.

train(self, X, Y, Z, U, num_targets=1)¶

Train the network using pairs of patches from the images.

Parameters:	X : array source scans, slices by height by width Y : array source labels, slices by height by width Z : array target scans, slices by height by width U : array target labels, slices by height by width, contains NaN’s at unknown labels num_targets : int How many target labels to use.
Returns:	None