niftynet.network.interventional_hybrid_net module

class INetHybridPreWarp(decay, affine_w_initializer=None, affine_b_initializer=None, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', interp='linear', boundary='replicate', name='inet-hybrid-pre-warp')

Bases: niftynet.network.base_net.BaseNet

__init__(decay, affine_w_initializer=None, affine_b_initializer=None, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', interp='linear', boundary='replicate', name='inet-hybrid-pre-warp')

Re-implementation of the registration network proposed in:

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

Parameters:

• decay –
• affine_w_initializer –
• affine_b_initializer –
• disp_w_initializer –
• disp_b_initializer –
• acti_func –
• interp –
• boundary –
• name –

layer_op(fixed_image, moving_image, is_training=True, **unused_kwargs)

class INetHybridTwoStream(decay, affine_w_initializer=None, affine_b_initializer=None, disp_w_initializer=None, disp_b_initializer=None, acti_func='relu', interp='linear', boundary='replicate', name='inet-hybrid-two-stream')

Bases: niftynet.network.base_net.BaseNet

layer_op(fixed_image, moving_image, is_training=True, **unused_kwargs)