loss¶
Pairwise Loss¶
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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)[source]¶
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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)[source]¶ 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
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bins¶
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bins_fixed_image¶
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sigma¶
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class
airlab.loss.pairwise.MSE(fixed_image, moving_image, fixed_mask=None, moving_mask=None, size_average=True, reduce=True)[source]¶ 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:
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class
airlab.loss.pairwise.NCC(fixed_image, moving_image, fixed_mask=None, moving_mask=None)[source]¶ - 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:
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class
airlab.loss.pairwise.NGF(fixed_image, moving_image, fixed_mask=None, moving_mask=None, epsilon=1e-05, size_average=True, reduce=True)[source]¶ 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
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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)[source]¶ 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