loss¶

Pairwise Loss¶

class airlab.loss.pairwise.LCC(fixed_image, moving_image, fixed_mask=None, moving_mask=None, sigma=3, kernel_type='box', size_average=True, reduce=True)¶

forward(displacement)¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class airlab.loss.pairwise.MI(fixed_image, moving_image, fixed_mask=None, moving_mask=None, bins=64, sigma=3, spatial_samples=0.1, background=None, size_average=True, reduce=True)¶

Implementation of the Mutual Information image loss.

\[\mathcal{S}_{\text{MI}} := H(F, M) - H(F|M) - H(M|F)\]

Parameters:

fixed_image (Image) – Fixed image for the registration
moving_image (Image) – Moving image for the registration
bins (int) – Number of bins for the intensity distribution
sigma (float) – Kernel sigma for the intensity distribution approximation
spatial_samples (float) – Percentage of pixels used for the intensity distribution approximation
background – Method to handle background pixels. None: Set background to the min value of image “mean”: Set the background to the mean value of the image float: Set the background value to the input value
size_average (bool) – Average loss function
reduce (bool) – Reduce loss function to a single value

bins¶

bins_fixed_image¶

forward(displacement)¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

sigma¶

class airlab.loss.pairwise.MSE(fixed_image, moving_image, fixed_mask=None, moving_mask=None, size_average=True, reduce=True)¶

The mean square error loss is a simple and fast to compute point-wise measure which is well suited for monomodal image registration.

\[\mathcal{S}_{\text{MSE}} := \frac{1}{\vert \mathcal{X} \vert}\sum_{x\in\mathcal{X}} \Big(I_M\big(x+f(x)\big) - I_F\big(x\big)\Big)^2\]

Parameters:	fixed_image (Image) – Fixed image for the registration moving_image (Image) – Moving image for the registration size_average (bool) – Average loss function reduce (bool) – Reduce loss function to a single value

forward(displacement)¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class airlab.loss.pairwise.NCC(fixed_image, moving_image, fixed_mask=None, moving_mask=None)¶

The normalized cross correlation loss is a measure for image pairs with a linear: intensity relation.

\[\mathcal{S}_{\text{NCC}} := \frac{\sum I_F\cdot (I_M\circ f) - \sum\text{E}(I_F)\text{E}(I_M\circ f)} {\vert\mathcal{X}\vert\cdot\sum\text{Var}(I_F)\text{Var}(I_M\circ f)}\]

Parameters:	fixed_image (Image) – Fixed image for the registration moving_image (Image) – Moving image for the registration

forward(displacement)¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class airlab.loss.pairwise.NGF(fixed_image, moving_image, fixed_mask=None, moving_mask=None, epsilon=1e-05, size_average=True, reduce=True)¶

Implementation of the Normalized Gradient Fields image loss.

Parameters:

fixed_image (Image) – Fixed image for the registration
moving_image (Image) – Moving image for the registration
fixed_mask (Tensor) – Mask for the fixed image
moving_mask (Tensor) – Mask for the moving image
epsilon (float) – Regulariser for the gradient amplitude
size_average (bool) – Average loss function
reduce (bool) – Reduce loss function to a single value

forward(displacement)¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class airlab.loss.pairwise.SSIM(fixed_image, moving_image, fixed_mask=None, moving_mask=None, sigma=[3], dim=2, kernel_type='box', alpha=1, beta=1, gamma=1, c1=1e-05, c2=1e-05, c3=1e-05, size_average=True, reduce=True)¶

Implementation of the Structual Similarity Image Measure loss.

Parameters:

fixed_image (Image) – Fixed image for the registration
moving_image (Image) – Moving image for the registration
fixed_mask (Tensor) – Mask for the fixed image
moving_mask (Tensor) – Mask for the moving image
sigma (float) – Sigma for the kernel
kernel_type (string) – Type of kernel i.e. gaussian, box
alpha (float) – Controls the influence of the luminance value
beta (float) – Controls the influence of the contrast value
gamma (float) – Controls the influence of the structure value
c1 (float) – Numerical constant for the luminance value
c2 (float) – Numerical constant for the contrast value
c3 (float) – Numerical constant for the structure value
size_average (bool) – Average loss function
reduce (bool) – Reduce loss function to a single value

forward(displacement)¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.