niftynet.network.interventional_affine_net module¶
-
class
INetAffine
(decay=1e-06, affine_w_initializer=None, affine_b_initializer=None, acti_func='relu', name='inet-affine')[source]¶ Bases:
niftynet.network.base_net.BaseNet
### Description This network estimates affine transformations from
a pair of moving and fixed image:
Hu et al., Label-driven weakly-supervised learning for multimodal deformable image registration, arXiv:1711.01666 https://arxiv.org/abs/1711.01666
Hu et al., Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration, Medical Image Analysis (2018) https://doi.org/10.1016/j.media.2018.07.002
- see also:
- https://github.com/YipengHu/label-reg
### Building blocks [DOWN CONV] - Convolutional layer + Residual Unit + Max pooling [CONV] - Convolutional layer [FC] - Fully connected layer, outputs the affine matrix [WARPER] - Grid resampling with the obtained affine matrix
### Diagram
INPUT PAIR –> [DOWN CONV]x4 –> [CONV] –> [FC] –> [WARPER] –> DISPLACEMENT FIELD
### Constraints - input spatial rank should be either 2 or 3 (2D or 3D images only)
-
__init__
(decay=1e-06, affine_w_initializer=None, affine_b_initializer=None, acti_func='relu', name='inet-affine')[source]¶ Parameters: - decay – float, regularisation decay
- affine_w_initializer – weight initialisation for affine registration network
- affine_b_initializer – bias initialisation for affine registration network
- acti_func – activation function to use
- name – layer name
-
layer_op
(fixed_image, moving_image, is_training=True, **unused_kwargs)[source]¶ Parameters: - fixed_image – tensor, fixed image for registration (defines reference space)
- moving_image – tensor, moving image to be registered to fixed
- is_training – boolean, True if network is in training mode
Returns: displacement fields transformed by estimating affine